document_title: List of Governors of Texas

question_text: who were the two female governors of texas

text: List of Governors of Texas

text: Governors of Spanish Texas[edit]

text: See: List of Texas Governors and Presidents

text: Governors of Mexican Texas[edit]

text: Presidents of the Republic of Texas[edit]

text: See: President of the Republic of Texas#List of presidents and vice presidents

text: Governors of Texas[edit]

text:
Number of Governors of Texas by party affiliation

Party
Governors

Democratic
39

Republican
7

Unionist
1

Independent
1

Military
1

text:
#
Governor
Term in office
Party
Election
Prior office
Lt. Governor

1

James Pinckney Henderson
March 31, 1808 - June 4, 1858
(Aged 50)
February 19, 1846
-
December 21, 1847
Democratic
1845
Minister to England and France Republic of Texas
(1837-1840)

Albert Clinton Horton

2

George T. Wood
March 12, 1795 - September 3, 1858
(Aged 63)
December 21, 1847
–
December 21, 1849
Democratic
1847
Texas State Senator
(1846-1847)

John Alexander Greer

3

Peter Hansborough Bell
May 11, 1810 - March 8, 1898
(Aged 87)
December 21, 1849
–
November 23, 1853
Democratic
1849
Lieutenant colonel of the Second Regiment Texas Mounted Volunteers
(1845-1949)

1851
[15]

James W. Henderson
[16]

4

James W. Henderson
August 15, 1817 - August 30, 1880
(Aged 63)
November 23, 1853
–
December 21, 1853
Democratic
3rd Lieutenant Governor of Texas
(1851-1853)
Office vacant

5

Elisha M. Pease
January 3, 1812 - August 26, 1883
(Aged 71)
December 21, 1853
–
December 21, 1857
Unionist
1853
Texas State Senator
(1849-1851)

David Catchings Dickson

1855

Hardin Richard Runnels

6

Hardin R. Runnels
August 30, 1820 - December 25, 1873
(Aged 53)
December 21, 1857
–
December 21, 1859
Democratic
1857
3rd Lieutenant Governor of Texas
(1855-1857)

Francis Lubbock

7

Sam Houston
March 2, 1793 - July 26, 1863
(Aged 70)
December 21, 1859
–
March 16, 1861
Independent
1859
[17]
U.S. Senator from Texas
(1846-1859)

Edward Clark
[16]

8

Edward Clark
April 1, 1815 - May 4, 1880
(Aged 65)
March 18, 1861
–
November 7, 1861
Democratic
7th Lieutenant Governor of Texas
(1859-1861)
Office vacant

9

Francis Lubbock
October 16, 1815 - June 22, 1905
(Aged 89)
November 7, 1861
–
November 5, 1863
Democratic
1861
6th Lieutenant Governor of Texas
(1857-1859)

John McClannahan Crockett

10

Pendleton Murrah
1824 or 1826 - August 4, 1865
(Aged about 40)
November 5, 1863
–
June 17, 1865
Democratic
1863
[18]
Texas State Representative
(1857)

Fletcher Summerfield Stockdale
[19]

-

Fletcher Summerfield Stockdale
1823 or 1825 - February 4, 1890
(Aged about 67)
June 11, 1865
–
June 16, 1865
Military
9th Lieutenant Governor of Texas
(1863-1865)
Office vacant

11

Andrew Jackson Hamilton
January 28, 1815 - April 11, 1875
(Aged 60)
June 16, 1865
–
August 9, 1866
[20]
Democratic-Military
Texas State Senator-Elect
(1861)

12

James W. Throckmorton
February 1, 1825 - April 21, 1894
(Aged 69)
August 9, 1866
–
August 8, 1867
Democratic
1866
[21]
Captain of the 6th Texas Cavalry Regiment Confederate Army
(1861-1863)

George Washington Jones

13

Elisha M. Pease
January 3, 1812 - August 26, 1883
(Aged 71)
August 8, 1867
–
September 30, 1869
[21][22]
Republican
5th Governor of Texas
(1853-1857)
Office vacant

14

Edmund J. Davis
October 2, 1827 - February 7, 1883
(Aged 55)
January 8, 1870
–
January 15, 1874
Republican
1869
[23]
Brigadier General of the First Texas Cavalry Regiment Union Army
(1862-1865)

15

Richard Coke
March 18, 1829 - May 14, 1897
(Aged 68)
January 15, 1874
–
December 1, 1876
Democratic
1873
Associate Justice of the Texas Supreme Court
(1866-1867)

Richard B. Hubbard
[16]

1876
[24]

16

Richard B. Hubbard
November 1, 1832 - July 12, 1901
(Aged 68)
December 1, 1876
–
January 21, 1879
Democratic
16th Lieutenant Governor of Texas
(1874-1876)
Office vacant

17

Oran Milo Roberts
July 9, 1815 – May 19, 1898
(Aged 82)
January 21, 1879
–
January 16, 1883
Democratic
1878
7th Chief Justice of the Texas Supreme Court
(1874-1879)

Joseph D. Sayers

1880

Leonidas Jefferson Storey

18

John Ireland
January 1, 1827 – March 15, 1896
(Aged 69)
January 16, 1883
–
January 18, 1887
Democratic
1882
Associate Justice of the Texas Supreme Court
(1875-1876)

Francis Marion Martin

1884

Barnett Gibbs

19

Lawrence Sullivan "Sul" Ross
September 27, 1838 – January 3, 1898
(Aged 59)
January 18, 1887
–
January 20, 1891
Democratic
1886
Texas State Senator
(1881-1883)

Thomas Benton Wheeler

1888

20

Jim Hogg
March 24, 1851 – March 3, 1906
(Aged 54)
January 20, 1891
–
January 15, 1895
Democratic
1890
21st Attorney General of Texas
(1887-1891)

George Cassety Pendleton

1892

Martin McNulty Crane

21

Charles A. Culberson
June 10, 1855 – March 19, 1925
(Aged 69)
January 15, 1895
–
January 17, 1899
Democratic
1894
22nd Attorney General of Texas
(1891-1895)

George Taylor Jester

1896

22

Joseph D. Sayers
September 23, 1841 – May 15, 1929
(Aged 87)
January 17, 1899
–
January 20, 1903
Democratic
1898
U.S. Representative for Texas' 9th district
(1893-1899)

James Browning

1900

23

S. W. T. Lanham
July 4, 1846 – July 29, 1908
(Aged 62)
January 20, 1903
–
January 15, 1907
Democratic
1902
U.S. Representative for Texas' 8th district
(1897-1903)

George D. Neal

1904

24

Thomas Mitchell Campbell
April 22, 1856 – April 1, 1923
(Aged 66)
January 15, 1907
–
January 17, 1911
Democratic
1906
General Manager of the Railroad
(1892-1897)

Asbury Bascom Davidson

1908

25

Oscar Branch Colquitt
December 16, 1861 – March 8, 1940
(Aged 78)
January 17, 1911
–
January 19, 1915
Democratic
1910
Texas Railroad Commissioner
(1903-1911)

1912

William Harding Mayes

26

James E. "Pa" Ferguson
August 31, 1871 – September 21, 1944
(Aged 73)
January 19, 1915
–
August 25, 1917
Democratic
1914
Local banker
(since 1906)

William P. Hobby
[25]

1916
[26]

27

William P. Hobby
March 26, 1878 – June 7, 1964
(Aged 86)
August 25, 1917
–
January 18, 1921
Democratic
24th Lieutenant Governor of Texas
(1915-1917)
Office vacant

1918

Willard Arnold Johnson

28

Pat Morris Neff
November 26, 1871 – January 20, 1952
(Aged 80)
January 18, 1921
–
January 20, 1925
Democratic
1920
Texas State Representative
(1899-1903)

Lynch Davidson

1922

Thomas Whitfield Davidson

29

Miriam A. "Ma" Ferguson
June 13, 1875 – June 25, 1961
(Aged 86)
January 20, 1925
–
January 18, 1927
Democratic
1924
First Lady of Texas
(1915–1917)

Barry Miller

30

Dan Moody
June 1, 1893 – May 22, 1966
(Aged 72)
January 18, 1927
–
January 20, 1931
Democratic
1926
32nd Attorney General of Texas
(1931–1935)

1928

31

Ross S. Sterling
February 11, 1875 – March 25, 1949
(Aged 74)
January 20, 1931
–
January 17, 1933
Democratic
1930
Chairman of the Texas Highway Commission
(1930)

Edgar E. Witt

32

Miriam A. "Ma" Ferguson
June 13, 1875 – June 25, 1961
(Aged 86)
January 17, 1933
–
January 15, 1935
Democratic
1932
29th Governor of Texas
(1925-1927)

33

James Allred
March 29, 1899 – September 24, 1959
(Aged 60)
January 15, 1935
–
January 17, 1939
Democratic
1934
35th Attorney General of Texas
(1931–1935)

Walter Frank Woodul

1936

34

Wilbert Lee "Pappy" O'Daniel
March 11, 1890 – May 11, 1969
(Aged 79)
January 17, 1939
–
August 4, 1941
Democratic
1938
President of the Fort Worth Chamber of Commerce
(1933-1934)

Coke R. Stevenson
[25]

1940
[27]

35

Coke R. Stevenson
March 20, 1888 – June 28, 1975
(Aged 87)
August 4, 1941
–
January 21, 1947
Democratic
31st Lieutenant Governor of Texas
(1939-1941)
Office vacant

1942

John Lee Smith

1944

36

Beauford H. Jester
January 12, 1893 – July 11, 1949
(Aged 56)
January 21, 1947
–
July 11, 1949
Democratic
1946
Member of the Texas Railroad Commission
(1943-1947)

Allan Shivers
[25]

1948
[28]

37

Allan Shivers
October 5, 1907 – January 14, 1985
(Aged 77)
July 11, 1949
–
January 15, 1957
Democratic
33rd Lieutenant Governor of Texas
(1947-1949)
Office vacant

1950

Ben Ramsey

1952

1954

38

Price Daniel
October 10, 1910 – August 25, 1988
(Aged 77)
January 15, 1957
–
January 15, 1963
Democratic
1956
U.S. Senator from Texas
(1953-1957)

1958

1960

Office vacant

39

John Connally
February 27, 1917 – June 15, 1993
(Aged 76)
January 15, 1963
–
January 21, 1969
Democratic
1962
56th United States Secretary of the Navy
(1961)

Preston Smith

1964

1966

40

Preston Smith
March 8, 1912 – October 18, 2003
(Aged 91)
January 21, 1969
–
January 16, 1973
Democratic
1968
35th Lieutenant Governor of Texas
(1963-1969)

Ben Barnes

1970

41

Dolph Briscoe
April 23, 1923 – June 27, 2010
(Aged 87)
January 16, 1973
–
January 16, 1979
Democratic
1972
Texas State Representative
(1949-1957)

William P. Hobby Jr.

1974

42

Bill Clements
April 13, 1917 – May 29, 2011
(Aged 94)
January 16, 1979
–
January 18, 1983
Republican
1978
15th United States Deputy Secretary of Defense
(1973-1977)

43

Mark White
March 17, 1940 – August 5, 2017
(Aged 77)
January 18, 1983
–
January 20, 1987
Democratic
1982
46th Attorney General of Texas
(1979-1983)

44

Bill Clements
April 13, 1917 – May 29, 2011
(Aged 94)
January 20, 1987
–
January 15, 1991
Republican
1986
42nd Governor of Texas
(1979-1983)

45

Ann Richards
September 1, 1933 – September 13, 2006
(Aged 73)
January 15, 1991
–
January 17, 1995
Democratic
1990
Treasurer of Texas
(1983-1991)

Bob Bullock

46

George W. Bush
July 6, 1946 (Age 71)
January 17, 1995
–
December 21, 2000
Republican
1994
Managing General Partner of the Texas Rangers
(1989-1994)

1998
[29]

Rick Perry

47

Rick Perry
March 4, 1950 (Age 67)
December 21, 2000
–
January 20, 2015
Republican
39th Lieutenant Governor of Texas
(1999-2000)

Bill Ratliff

2002

David Dewhurst

2006

2010

48

Greg Abbott
November 13, 1957 (Age 59)
January 20, 2015
–
Incumbent
Republican
2014
[30]
50th Attorney General of Texas
(2002-2015)

Dan Patrick

text: Other high offices held[edit]

text:
Governor
Gubernatorial Term
Other high offices held

James Pinckney Henderson
1846–1847
U.S. Senator (1857–1858)

Peter Hansborough Bell
1849–1853
U.S. Representative (1853–1857)

Sam Houston
1859–1861
U.S. Representative from Tennessee (1823–1827), Governor of Tennessee (1827–1829), President of Texas (1836–1838, 1841–1844), U.S. Senator (1846–1859)

Andrew Jackson Hamilton
1865–1866
U.S. Representative (1859–1861)

James W. Throckmorton
1866–1867
U.S. Representative (1875–1879, 1883–1887)

Richard Coke
1874–1876
U.S. Senator (1877–1895)

Richard B. Hubbard
1876–1879
Minister to Japan (1885–1889)

Charles Allen Culberson
1895–1899
U.S. Senator (1899–1923)

Joseph D. Sayers
1899–1903
U.S. Representative (1885–1893, 1893–1899)

S. W. T. Lanham
1903–1907
U.S. Representative (1883–1893, 1897–1903)

W. Lee O'Daniel
1939–1941
U.S. Senator (1941–1949)

Price Daniel
1957–1963
U.S. Senator (1953–1957)

John Connally
1963–1969
U.S. Secretary of the Navy (1961), U.S. Secretary of the Treasury (1971–1972)

Bill Clements
1979–1983
1987–1991
U.S. Deputy Secretary of Defense (1973–1977)

George W. Bush
1995–2000
43rd President of the United States (2001–2009)

Rick Perry
2000–2015
United States Secretary of Energy

text: Living former governors of Texas[edit]

text: Currently, there are two living former governors of Texas. The most recent death of a former governor was that of Mark White (1983–1987), who died on August 5, 2017. The most recently serving governor of Texas who has died is Ann Richards (1991-1995, born 1933), who died on September 13, 2006. Pictured in order of service:

text:
George W. Bush
(1995–2000)
(1946-07-06) July 6, 1946 (age 71)

Rick Perry
(2000–2015)
(1950-03-04) March 4, 1950 (age 67)

text: Gubernatorial trivia[edit]

text: Background[edit]

text: Texas has had two female governors: Miriam A. "Ma" Ferguson and Ann Richards. Ferguson was one of the first two women elected governor of a U.S. state (on November 4, 1924), along with Nellie Tayloe Ross of Wyoming. Ross was inaugurated on January 5, 1925, while Ferguson was inaugurated on January 20, so Ross is considered the first female state governor. Ferguson was the wife of former governor Jim "Pa" Ferguson, while Richards was elected "in her own right," being neither the spouse nor widow of a governor.

text: Texas governors have been born in fourteen states: Alabama, Connecticut, Florida, Georgia, Iowa, Kentucky, Louisiana, Mississippi, North Carolina, Ohio, South Carolina, Tennessee, Texas, and Virginia.

text: Baylor University is the most common alma mater of Texas governors, with five of them - Lawrence Sullivan Ross, Pat Morris Neff, Price Daniel, Mark White, and Ann Richards - considered alumni (though Ross attended but never completed a degree). To date, Coke Stevenson is the most recent governor who never attended college, and Bill Clements is the most recent who attended college but did not graduate.

text: Elections[edit]

text: Three governors have served non-consecutive terms: Elisha M. Pease, Miriam A. Ferguson, and Bill Clements. As was the case in most Southern states, Texas did not elect any Republican governor from the end of Reconstruction until the late twentieth century. Bill Clements was the state's first Republican governor since Edmund J. Davis left office in 1874, 105 years earlier. Dolph Briscoe was the last governor to be elected to a two-year term, in 1972; he was also the first to be elected to a four-year term, in 1974, since the post-Reconstruction period when two-year terms had first been established. Rick Perry, who ascended to the governorship on December 21, 2000 upon the resignation of then-Governor George W. Bush, won full four-year terms in 2002, 2006 and 2010.

text: Texas governors in popular culture[edit]

text: W. Lee "Pappy" O'Daniel served as the inspiration for the fictional, but similarly named, Mississippi Governor Menelaus "Pappy" O'Daniel, in the film O Brother, Where Art Thou?

text: Ann Richards had a cameo appearance on an episode of the animated comedy series King of the Hill, in which she has a brief romance with Bill Dauterive after he takes the fall for mooning her in the elevator of an Austin hotel (Hank actually mooned her because he thought his friends were going to be mooning the people in the elevator but they set him up).


document_title: Statistical inference

question_text: in doing statistical inference data are gathered by the process of

root

text: Statistical inference

text: Introduction[edit]

text: Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling. Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.[citation needed]

tag: p

text: Konishi & Kitagawa state, "The majority of the problems in statistical inference can be considered to be problems related to statistical modeling".[2] Relatedly, Sir David Cox has said, "How [the] translation from subject-matter problem to statistical model is done is often the most critical part of an analysis".[3]

text: The conclusion of a statistical inference is a statistical proposition.[citation needed] Some common forms of statistical proposition are the following:

text:
a point estimate, i.e. a particular value that best approximates some parameter of interest;
an interval estimate, e.g. a confidence interval (or set estimate), i.e. an interval constructed using a dataset drawn from a population so that, under repeated sampling of such datasets, such intervals would contain the true parameter value with the probability at the stated confidence level;
a credible interval, i.e. a set of values containing, for example, 95% of posterior belief;
rejection of a hypothesis;[a]
clustering or classification of data points into groups.

tag: ul

text: Models and assumptions[edit]

text: Any statistical inference requires some assumptions. A statistical model is a set of assumptions concerning the generation of the observed data and similar data. Descriptions of statistical models usually emphasize the role of population quantities of interest, about which we wish to draw inference.[4] Descriptive statistics are typically used as a preliminary step before more formal inferences are drawn.[5]

text: Degree of models/assumptions[edit]

text: Statisticians distinguish between three levels of modeling assumptions;

text:
Fully parametric: The probability distributions describing the data-generation process are assumed to be fully described by a family of probability distributions involving only a finite number of unknown parameters.[4] For example, one may assume that the distribution of population values is truly Normal, with unknown mean and variance, and that datasets are generated by 'simple' random sampling. The family of generalized linear models is a widely used and flexible class of parametric models.
Non-parametric: The assumptions made about the process generating the data are much less than in parametric statistics and may be minimal.[6] For example, every continuous probability distribution has a median, which may be estimated using the sample median or the Hodges–Lehmann–Sen estimator, which has good properties when the data arise from simple random sampling.
Semi-parametric: This term typically implies assumptions 'in between' fully and non-parametric approaches. For example, one may assume that a population distribution has a finite mean. Furthermore, one may assume that the mean response level in the population depends in a truly linear manner on some covariate (a parametric assumption) but not make any parametric assumption describing the variance around that mean (i.e. about the presence or possible form of any heteroscedasticity). More generally, semi-parametric models can often be separated into 'structural' and 'random variation' components. One component is treated parametrically and the other non-parametrically. The well-known Cox model is a set of semi-parametric assumptions.

text: Importance of valid models/assumptions[edit]

text: Whatever level of assumption is made, correctly calibrated inference in general requires these assumptions to be correct; i.e. that the data-generating mechanisms really have been correctly specified.

text: Incorrect assumptions of 'simple' random sampling can invalidate statistical inference.[7] More complex semi- and fully parametric assumptions are also cause for concern. For example, incorrectly assuming the Cox model can in some cases lead to faulty conclusions.[8] Incorrect assumptions of Normality in the population also invalidates some forms of regression-based inference.[9] The use of any parametric model is viewed skeptically by most experts in sampling human populations: "most sampling statisticians, when they deal with confidence intervals at all, limit themselves to statements about [estimators] based on very large samples, where the central limit theorem ensures that these [estimators] will have distributions that are nearly normal."[10] In particular, a normal distribution "would be a totally unrealistic and catastrophically unwise assumption to make if we were dealing with any kind of economic population."[10] Here, the central limit theorem states that the distribution of the sample mean "for very large samples" is approximately normally distributed, if the distribution is not heavy tailed.

text: Approximate distributions[edit]

text: Given the difficulty in specifying exact distributions of sample statistics, many methods have been developed for approximating these.

text: With finite samples, approximation results measure how close a limiting distribution approaches the statistic's sample distribution: For example, with 10,000 independent samples the normal distribution approximates (to two digits of accuracy) the distribution of the sample mean for many population distributions, by the Berry–Esseen theorem.[11] Yet for many practical purposes, the normal approximation provides a good approximation to the sample-mean's distribution when there are 10 (or more) independent samples, according to simulation studies and statisticians' experience.[11] Following Kolmogorov's work in the 1950s, advanced statistics uses approximation theory and functional analysis to quantify the error of approximation. In this approach, the metric geometry of probability distributions is studied; this approach quantifies approximation error with, for example, the Kullback–Leibler divergence, Bregman divergence, and the Hellinger distance.[12][13][14]

text: With indefinitely large samples, limiting results like the central limit theorem describe the sample statistic's limiting distribution, if one exists. Limiting results are not statements about finite samples, and indeed are irrelevant to finite samples.[15][16][17] However, the asymptotic theory of limiting distributions is often invoked for work with finite samples. For example, limiting results are often invoked to justify the generalized method of moments and the use of generalized estimating equations, which are popular in econometrics and biostatistics. The magnitude of the difference between the limiting distribution and the true distribution (formally, the 'error' of the approximation) can be assessed using simulation.[18] The heuristic application of limiting results to finite samples is common practice in many applications, especially with low-dimensional models with log-concave likelihoods (such as with one-parameter exponential families).

text: Randomization-based models[edit]

text: For a given dataset that was produced by a randomization design, the randomization distribution of a statistic (under the null-hypothesis) is defined by evaluating the test statistic for all of the plans that could have been generated by the randomization design. In frequentist inference, randomization allows inferences to be based on the randomization distribution rather than a subjective model, and this is important especially in survey sampling and design of experiments.[19][20] Statistical inference from randomized studies is also more straightforward than many other situations.[21][22][23] In Bayesian inference, randomization is also of importance: in survey sampling, use of sampling without replacement ensures the exchangeability of the sample with the population; in randomized experiments, randomization warrants a missing at random assumption for covariate information.[24]

text: Objective randomization allows properly inductive procedures.[25][26][27][28] Many statisticians prefer randomization-based analysis of data that was generated by well-defined randomization procedures.[29] (However, it is true that in fields of science with developed theoretical knowledge and experimental control, randomized experiments may increase the costs of experimentation without improving the quality of inferences.[30][31]) Similarly, results from randomized experiments are recommended by leading statistical authorities as allowing inferences with greater reliability than do observational studies of the same phenomena.[32] However, a good observational study may be better than a bad randomized experiment.

text: The statistical analysis of a randomized experiment may be based on the randomization scheme stated in the experimental protocol and does not need a subjective model.[33][34]

text: However, at any time, some hypotheses cannot be tested using objective statistical models, which accurately describe randomized experiments or random samples. In some cases, such randomized studies are uneconomical or unethical.

text: Model-based analysis of randomized experiments[edit]

text: It is standard practice to refer to a statistical model, often a linear model, when analyzing data from randomized experiments. However, the randomization scheme guides the choice of a statistical model. It is not possible to choose an appropriate model without knowing the randomization scheme.[20] Seriously misleading results can be obtained analyzing data from randomized experiments while ignoring the experimental protocol; common mistakes include forgetting the blocking used in an experiment and confusing repeated measurements on the same experimental unit with independent replicates of the treatment applied to different experimental units.[35]

text: Paradigms for inference[edit]

text: Different schools of statistical inference have become established. These schools—or "paradigms"—are not mutually exclusive, and methods that work well under one paradigm often have attractive interpretations under other paradigms.

text: Bandyopadhyay & Forster[36] describe four paradigms: "(i) classical statistics or error statistics, (ii) Bayesian statistics, (iii) likelihood-based statistics, and (iv) the Akaikean-Information Criterion-based statistics". The classical (or frequentist) paradigm, the Bayesian paradigm, and the AIC-based paradigm are summarized below. The likelihood-based paradigm is essentially a sub-paradigm of the AIC-based paradigm.

text: Frequentist inference[edit]

text: This paradigm calibrates the plausibility of propositions by considering (notional) repeated sampling of a population distribution to produce datasets similar to the one at hand. By considering the dataset's characteristics under repeated sampling, the frequentist properties of a statistical proposition can be quantified—although in practice this quantification may be challenging.

text: Examples of frequentist inference[edit]

text:
p-value
Confidence interval

text: Frequentist inference, objectivity, and decision theory[edit]

text: One interpretation of frequentist inference (or classical inference) is that it is applicable only in terms of frequency probability; that is, in terms of repeated sampling from a population. However, the approach of Neyman[37] develops these procedures in terms of pre-experiment probabilities. That is, before undertaking an experiment, one decides on a rule for coming to a conclusion such that the probability of being correct is controlled in a suitable way: such a probability need not have a frequentist or repeated sampling interpretation. In contrast, Bayesian inference works in terms of conditional probabilities (i.e. probabilities conditional on the observed data), compared to the marginal (but conditioned on unknown parameters) probabilities used in the frequentist approach.

text: The frequentist procedures of significance testing and confidence intervals can be constructed without regard to utility functions. However, some elements of frequentist statistics, such as statistical decision theory, do incorporate utility functions.[citation needed] In particular, frequentist developments of optimal inference (such as minimum-variance unbiased estimators, or uniformly most powerful testing) make use of loss functions, which play the role of (negative) utility functions. Loss functions need not be explicitly stated for statistical theorists to prove that a statistical procedure has an optimality property.[38] However, loss-functions are often useful for stating optimality properties: for example, median-unbiased estimators are optimal under absolute value loss functions, in that they minimize expected loss, and least squares estimators are optimal under squared error loss functions, in that they minimize expected loss.

text: While statisticians using frequentist inference must choose for themselves the parameters of interest, and the estimators/test statistic to be used, the absence of obviously explicit utilities and prior distributions has helped frequentist procedures to become widely viewed as 'objective'.[citation needed]

text: Bayesian inference[edit]

text: The Bayesian calculus describes degrees of belief using the 'language' of probability; beliefs are positive, integrate to one, and obey probability axioms. Bayesian inference uses the available posterior beliefs as the basis for making statistical propositions. There are several different justifications for using the Bayesian approach.

text: Examples of Bayesian inference[edit]

text:
Credible interval for interval estimation
Bayes factors for model comparison

text: Bayesian inference, subjectivity and decision theory[edit]

text: Many informal Bayesian inferences are based on "intuitively reasonable" summaries of the posterior. For example, the posterior mean, median and mode, highest posterior density intervals, and Bayes Factors can all be motivated in this way. While a user's utility function need not be stated for this sort of inference, these summaries do all depend (to some extent) on stated prior beliefs, and are generally viewed as subjective conclusions. (Methods of prior construction which do not require external input have been proposed but not yet fully developed.)

text: Formally, Bayesian inference is calibrated with reference to an explicitly stated utility, or loss function; the 'Bayes rule' is the one which maximizes expected utility, averaged over the posterior uncertainty. Formal Bayesian inference therefore automatically provides optimal decisions in a decision theoretic sense. Given assumptions, data and utility, Bayesian inference can be made for essentially any problem, although not every statistical inference need have a Bayesian interpretation. Analyses which are not formally Bayesian can be (logically) incoherent; a feature of Bayesian procedures which use proper priors (i.e. those integrable to one) is that they are guaranteed to be coherent. Some advocates of Bayesian inference assert that inference must take place in this decision-theoretic framework, and that Bayesian inference should not conclude with the evaluation and summarization of posterior beliefs.

text: AIC-based inference[edit]

text: This section needs expansion. You can help by adding to it. (November 2017)

tag: table

text: The Akaike information criterion (AIC) is an estimator of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection.

text: AIC is founded on information theory: it offers an estimate of the relative information lost when a given model is used to represent the process that generated the data. (In doing so, it deals with the trade-off between the goodness of fit of the model and the simplicity of the model.)

text: Other paradigms for inference[edit]

text: Minimum description length[edit]

text: The minimum description length (MDL) principle has been developed from ideas in information theory[39] and the theory of Kolmogorov complexity.[40] The (MDL) principle selects statistical models that maximally compress the data; inference proceeds without assuming counterfactual or non-falsifiable "data-generating mechanisms" or probability models for the data, as might be done in frequentist or Bayesian approaches.

text: However, if a "data generating mechanism" does exist in reality, then according to Shannon's source coding theorem it provides the MDL description of the data, on average and asymptotically.[41] In minimizing description length (or descriptive complexity), MDL estimation is similar to maximum likelihood estimation and maximum a posteriori estimation (using maximum-entropy Bayesian priors). However, MDL avoids assuming that the underlying probability model is known; the MDL principle can also be applied without assumptions that e.g. the data arose from independent sampling.[41][42]

text: The MDL principle has been applied in communication-coding theory in information theory, in linear regression,[42] and in data mining.[40]

text: The evaluation of MDL-based inferential procedures often uses techniques or criteria from computational complexity theory.[43]

text: Fiducial inference[edit]

text: Fiducial inference was an approach to statistical inference based on fiducial probability, also known as a "fiducial distribution". In subsequent work, this approach has been called ill-defined, extremely limited in applicability, and even fallacious.[44][45] However this argument is the same as that which shows[46] that a so-called confidence distribution is not a valid probability distribution and, since this has not invalidated the application of confidence intervals, it does not necessarily invalidate conclusions drawn from fiducial arguments. An attempt was made to reinterpret the early work of Fisher's fiducial argument as a special case of an inference theory using Upper and lower probabilities.[47]

text: Structural inference[edit]

text: Developing ideas of Fisher and of Pitman from 1938 to 1939,[48] George A. Barnard developed "structural inference" or "pivotal inference",[49] an approach using invariant probabilities on group families. Barnard reformulated the arguments behind fiducial inference on a restricted class of models on which "fiducial" procedures would be well-defined and useful.

text: Inference topics[edit]

text: The topics below are usually included in the area of statistical inference.

text:
Statistical assumptions
Statistical decision theory
Estimation theory
Statistical hypothesis testing
Revising opinions in statistics
Design of experiments, the analysis of variance, and regression
Survey sampling
Summarizing statistical data

tag: ol


document_title: Trippie Redd

question_text: when did a love letter to you drop

question_tokens: when did a love letter to you drop

text: Trippie Redd

text: Early life[edit]

text: Michael White was born on June 18, 1999, in Canton, Ohio. White's father was in prison at the time of his birth, and his mother was left to raise him as a single parent.[2] White grew up in Canton though he moved to Columbus, Ohio, several times.[3] White's interest in music began when his mother played Ashanti, Beyoncé, Tupac, and Nas when White was growing up and it led to him later listening to artists such as T-Pain, KISS, Gucci Mane, Marilyn Manson and Lil Wayne.[3][2] White began rapping after being inspired by Taevion Williams, another rapper who went by the stage name Lil Tae.[3] Williams later died in a car crash.[4] White began taking his music career seriously and began recording music, releasing “Sub-Zero” and “New Ferrari” in 2014, soon deleting those songs.[2]

text: White was also an affiliate of the Bloods street gang, and attended high school in Canton where he achieved a 4.0 GPA, describing himself during high school as a "loner" who was "always popular".[3][4][5][6] Following his graduation, White moved to Atlanta, where he met rapper Lil Wop[7] and was eventually offered a deal with a record label.[8]

text: Career[edit]

text: Lil Wop helped White get started with a professional recording studio, where he began working with Lil Wop together with Kodie Shane and recorded three projects titled Awakening My Inner Beast, Beast Mode and Rock the World Trippie.[2][8] White eventually signed to the label Strainge Entertainment (now known as Elliot Grainge Entertainment) and relocated to Los Angeles.[4][9]

text: In May 2017, White released his debut mixtape, A Love Letter to You, with the lead single "Love Scars", which received more than 8 million views on YouTube within several months.[10][11] and more than 13 million on Soundcloud.[12] White was included on XXXTentacion's album 17, on the song "Fuck Love" which peaked at number 41 on the Billboard Hot 100.[13][14]

text: In October 2017, White released his second mixtape, A Love Letter to You II.[15] The album debuted at #34 on the Billboard 200. Later in the month, White released a collaboration EP with Atlanta rap artist Lil Wop titled Angels & Demons.[16]

text: On December 5, 2017, White released the song "Dark Knight Dummo", which features Travis Scott.[17] The song peaked at 72 on the Billboard Hot 100, making it White's first entry on the chart as a lead artist.[18] On December 25, 2017, White released the song "TR666" on his Soundcloud account, which features Swae Lee and was produced by Scott Storch.[19] The track was previewed on November 30.[20] White released "18" alongside Baauer, Kris Wu, Joji and Rich Brian.[21]

text: Controversies[edit]

text: Feuds[edit]

text: In April 2017, 6ix9ine featured on the gold certified Trippie Redd song, "Poles1469" and in July 2017, he was featured on the Trippie Redd song "Owee."[22] Following a post from a Twitter account claiming 6ix9ine was a pedophile, White denounced 6ix9ine, saying "I’m sorry brozay, 1400 don’t promote pedophiles… If we give niggas clout, we give niggas clout. It was an accident."[23] On November 11, 2017, White was attacked in a New York hotel and whilst speaking on it using Instagram's livestream feature, claimed that a member of 6ix9ine's team ambushed him,[24] 6ix9ine later confirmed his involvement.[25]

text: 6ix9ine and Trippie Redd continued to trade insults over social media in February 2018 and March 2018.[26]

text: XXXTENTACION[edit]

text: In October 2017, a preview of the song "God's Plan" by Drake was shown on social media. The song originally featured Trippie Redd singing the hook and giving an additional verse.[27] Frequent collaborator, Florida rapper XXXTentacion, who had prior issues with Drake[28], began to associate with New York rapper 6ix9ine to the distaste of White.[29] In March 2018, XXXTentacion "banned" White from Florida, promising to assault him if he attempted to enter the state. XXXTentacion later apologized to White during a performance later that month leading the two to reconcile after White accepted his apology via a post on his Instagram account. [30]

text: Personal life[edit]

text: White is of partial Irish and Native American descent.

text: White claimed in March 2017 that he was worth $7 million and proceeded to buy his mother a $300,000 home.[31] The claim was contested by other rappers including 6ix9ine.

text: Discography[edit]

text: Mixtapes[edit]

text:
A Love Letter to You (2017)[32]
A Love Letter to You 2 (2017)

text: EPs[edit]

text:
Awakening My InnerBeast (2016)
Beast Mode (2016)
Rock the World, Trippie (2016)
White Room Project (2017)
A Love Letter to Myron (2017)
Angels and Demons (with Lil Wop) (2017)

text: Singles[edit]

text: As lead artist[edit]

text:
Title
Year
Peak chart positions
Certifications
Album

US
[33][34]
US
R&B/HH
[35]
CAN
[36]

"Poles1469"
(featuring 6ix9ine)
2017
—[A]
44
—

RIAA: Gold[1]

A Love Letter to You[38]

"Love Scars"
—[B]
—[C]
—

RIAA: Gold[1]

"Dark Knight Dummo"
(featuring Travis Scott)
72
29
93

TBA

text: As featured artist[edit]

text:
List of singles as a featured artist, with selected chart positions, showing year released and album name

Title
Year
Peak chart positions
Album

US
[13]
US
R&B/HH
[40]
CAN
[41]
ITA
[42]
NZ
Heat.
[43]
SWE
[44]
SWI
[45]

"Ill Nana"[46]
(DRAM featuring Trippie Redd)
2017
—
—
—
—
—
—
—
Big Baby D.R.A.M.

"18"
(Kris Wu, Rich Brian, Joji featuring Tripple Redd and Baauer)
2018
—
—
—
—
—
—
—
Non-album single

"Fuck Love"[47]
(XXXTentacion featuring Trippie Redd)
41
18
52
73
2
—[D]
88
17

text: Notes[edit]

text:
Jump up ^ "Poles1469" did not enter the Billboard Hot 100, but peaked at number 8 on the Bubbling Under Hot 100 Singles chart.[37]
Jump up ^ "Love Scars" did not enter the Billboard Hot 100, but peaked at number 15 on the Bubbling Under Hot 100 Singles chart.[37]
Jump up ^ "Love Scars" did not enter the Billboard Hot R&B/Hip-Hop Songs chart, but peaked at number one on the Bubbling Under R&B/Hip-Hop Singles chart.[39]
Jump up ^ "Fuck Love" did not enter the Sverigetopplistan singles chart, but peaked at number one on the Sverigetopplistan Heatseeker chart.[48]


document_title: Neural tube

question_text: what is the function of the proteins secreted by cells of the ventral midline of the spinal cord

text: Neural tube

text: Development[edit]

text: The neural tube develops in two ways: primary neurulation and secondary neurulation.

text: Primary neurulation divides the ectoderm into three cell types:

text:
The internally located neural tube
The externally located epidermis
The neural crest cells, which develop in the region between the neural tube and epidermis but then migrate to new locations

text: Primary neurulation begins after the neural plate forms. The edges of the neural plate start to thicken and lift upward, forming the neural folds. The center of the neural plate remains grounded, allowing a U-shaped neural groove to form. This neural groove sets the boundary between the right and left sides of the embryo. The neural folds pinch in towards the midline of the embryo and fuse together to form the neural tube.[1]

text: In secondary neurulation, the cells of the neural plate form a cord-like structure that migrates inside the embryo and hollows to form the tube.

text: Each organism uses primary and secondary neurulation to varying degrees.

text:
Neurulation in fish proceeds only via the secondary form.
In avian species the posterior regions of the tube develop using secondary neurulation and the anterior regions develop by primary neurulation.
In mammals, secondary neurulation begins around the 35th somite.

text: Mammalian neural tubes close in the head in the opposite order that they close in the trunk.

text: In the head:

text:
Neural crest cells migrate
Neural tube closes
Overlying ectoderm closes

text: In the trunk:

text:
Overlying ectoderm closes
Neural tube closes
Neural crest cells migrate

text: Structure[edit]

text: Four neural tube subdivisions each eventually develop into distinct regions of the central nervous system by the division of neuroepithelial cells: the forebrain (prosencephalon), the midbrain (mesencephalon), the hindbrain (rhombencephalon) and the spinal cord.

text:
The prosencephalon further goes on to develop into the telencephalon (cerebrum) and the diencephalon (the optic vesicles and hypothalamus).
The mesencephalon stays as the midbrain.
The rhombencephalon develops into the metencephalon (the pons and cerebellum) and the myelencephalon (the medulla oblongata).

text: For a short time, the neural tube is open both cranially and caudally. These openings, called neuropores, close during the fourth week in humans. Improper closure of the neuropores can result in neural tube defects such as anencephaly or spina bifida.

text: The dorsal part of the neural tube contains the alar plate, which is associated primarily with sensation. The ventral part of the neural tube contains the basal plate, which is primarily associated with motor (i.e., muscle) control.

text: Dorsal-ventral patterning[edit]

text: The neural tube patterns along the dorsal-ventral axis to establish defined compartments of neural progenitor cells that lead to distinct classes of neurons.[2] According to the French flag model of morphogenesis, this patterning occurs early in development and results from the activity of several secreted signaling molecules. Sonic hedgehog (Shh) is a key player in patterning the ventral axis, while bone morphogenic proteins (BMPs) and Wnt family members play an important role in patterning the dorsal axis.[3] Other factors shown to provide positional information to the neural progenitor cells include fibroblast growth factors (FGFs) and retinoic acid. Retinoic acid is required ventrally along with Shh to induce Pax6 and Olig2 during differentiation of motor neurons.[4]

text: Three main ventral cell types are established during early neural tube development: the floor plate cells, which form at the ventral midline during the neural fold stage; as well as the more dorsally located motor neurons and interneurons.[2] These cell types are specified by the secretion of the Shh from the notochord (located ventrally to the neural tube), and later from the floor plate cells.[5] Shh acts as a morphogen, meaning that it acts in a concentration-dependent manner to specify cell types as it moves further from its source.[6]

text: The following is a proposed mechanism for how Shh patterns the ventral neural tube: A gradient of Shh that controls the expression of a group of homeodomain (HD) and basic Helix-Loop-Helix (bHLH) transcription factors is created. These transcription factors are grouped into two protein classes based on how Shh affects them. Class I is inhibited by Shh, whereas Class II is activated by Shh. These two classes of proteins then cross-regulate each other to create more-defined boundaries of expression. The different combinations of expression of these transcription factors along the dorsal-ventral axis of the neural tube are responsible for creating the identity of the neuronal progenitor cells.[3] Five molecularly distinct groups of ventral neurons form from these neuronal progenitor cells in vitro. Also, the position at which these neuronal groups are generated in vivo can be predicted by the concentration of Shh required for their induction in vitro.[7] Studies have shown that neural progenitors can evoke different responses based on the length of exposure to Shh, with a longer exposure time resulting in more ventral cell types.[8][9]

text: At the dorsal end of the neural tube, BMPs are responsible for neuronal patterning. BMP is initially secreted from the overlying ectoderm. A secondary signaling center is then established in the roof plate, the dorsal most structure of the neural tube.[1] BMP from the dorsal end of the neural tube seems to act in the same concentration-dependent manner as Shh in the ventral end.[10] This was shown using zebrafish mutants that had varying amounts of BMP signaling activity. Researchers observed changes in dorsal-ventral patterning, for example zebrafish deficient in certain BMPs showed a loss of dorsal sensory neurons and an expansion of interneurons.[11]


document_title: Maslow's hierarchy of needs

question_text: the base level of maslow's hierarchy of needs pyramid includes

question_tokens: the base level of maslow 's hierarchy of needs pyramid includes

text: Maslow's hierarchy of needs

text: Hierarchy[edit]

text: Maslow's hierarchy of needs is  often portrayed in the shape of a pyramid with the largest, most fundamental needs at the bottom and the need for self-actualization and self-transcendence at the top.[1][7]

text: The most fundamental and basic four layers of the pyramid contain what Maslow called "deficiency needs" or "d-needs": esteem, friendship and love, security, and physical needs. If these "deficiency needs" are not met – with the exception of the most fundamental (physiological) need – there may not be a physical indication, but the individual will feel anxious and tense. Maslow's theory suggests that the most basic level of needs must be met before the individual will strongly desire (or focus motivation upon) the secondary or higher level needs. Maslow also coined the term "metamotivation" to describe the motivation of people who go beyond the scope of the basic needs and strive for constant betterment.[8]

text: The human brain is a complex system and has parallel processes running at the same time, thus many different motivations from various levels of Maslow's hierarchy can occur at the same time. Maslow spoke clearly about these levels and their satisfaction in terms such as "relative", "general", and "primarily". Instead of stating that the individual focuses on a certain need at any given time, Maslow stated that a certain need "dominates" the human organism.[5] Thus Maslow acknowledged the likelihood that the different levels of motivation could occur at any time in the human mind, but he focused on identifying the basic types of motivation and the order in which they would tend to be met.

text: Physiological needs[edit]

text: Physiological need is a concept that was derived to explain and cultivate the foundation for motivation. This concept is the basic foundation of Maslow’s hierarchy of needs. This term was coined to represent a similar premise as drive[3]. Physiological needs are considered the main physical requirements for human survival. This means that Physiological needs are universal human needs. Physiological needs are considered the first step in internal motivation according to Maslow’s hierarchy of needs. This theory states that humans are compelled to fulfill these physiological needs first in order to pursue intrinsic satisfaction on a higher level[3]. If these needs are not achieved, it leads to an increase in displeasure within an individual. In return, when individuals feel this increase in displeasure, the motivation to decrease these discrepancies increases[3]. Physiological needs can be defined as both traits and a state[3]. Physiological needs as traits allude to long-term, unchanging demands that are required of basic human life. Physiological needs as a state alludes to the unpleasant decrease in pleasure and the increase for an incentive to fulfill a necessity[3]. In order to pursue intrinsic motivation higher up Maslow’s hierarchy, Physiological needs must be met first. This means that if a human is struggling to meet their physiological needs, then they are unlikely to intrinsically pursue safety, belongingness, esteem, and self-actualization. Physiological needs include:

text:
Homeostasis
Food
Water
Sleep
Shelter

text: Safety needs[edit]

text: Once a person's physiological needs are relatively satisfied, their safety needs take precedence and dominate behavior. In the absence of physical safety – due to war, natural disaster, family violence, childhood abuse, etc. – people may (re-)experience post-traumatic stress disorder or transgenerational trauma. In the absence of economic safety – due to economic crisis and lack of work opportunities – these safety needs manifest themselves in ways such as a preference for job security, grievance procedures for protecting the individual from unilateral authority, savings accounts, insurance policies, disability accommodations, etc. This level is more likely to predominate in children as they generally have a greater need to feel safe. Safety and security needs are about keeping us safe from harm. These include shelter, job security, health, and safe environments. If a person does not feel safe in an environment, they will seek to find safety before they attempt to meet any higher level of survival, but the need for safety is not as important as basic physiological needs.

text: Safety and Security needs include:

text: Personal security

text:
Financial security
Health and well-being
Safety needs against accidents/illness and their adverse impacts

text: Social belonging[edit]

text: After physiological and safety needs are fulfilled, the third level of human needs are seen to be interpersonal and involves feelings of belongingness. This need is especially strong in childhood and it can override the need for safety as witnessed in children who cling to abusive parents. Deficiencies within this level of Maslow's hierarchy – due to hospitalism, neglect, shunning, ostracism, etc. – can adversely affect the individual's ability to form and maintain emotionally significant relationships in general.

text: Social Belonging needs include:

text:
Friendships
Intimacy
Family

text: According to Maslow, humans need to feel a sense of belonging and acceptance among social groups, regardless whether these groups are large or small. For example, some large social groups may include clubs, co-workers, religious groups, professional organizations, sports teams, gangs, and online communities. Some examples of small social connections include family members, intimate partners, mentors, colleagues, and confidants. Humans need to love and be loved – both sexually and non-sexually – by others.[2] Many people become susceptible to loneliness, social anxiety, and clinical depression in the absence of this love or belonging element. This need for belonging may overcome the physiological and security needs, depending on the strength of the peer pressure.

text: Esteem[edit]

text: Esteem needs are ego needs or status needs. People develop a concern with getting recognition, status, importance, and respect from others. Most humans have a need to feel respected; this includes the need to have self-esteem and self-respect. Esteem presents the typical human desire to be accepted and valued by others. People often engage in a profession or hobby to gain recognition. These activities give the person a sense of contribution or value. Low self-esteem or an inferiority complex may result from imbalances during this level in the hierarchy. People with low self-esteem often need respect from others; they may feel the need to seek fame or glory. However, fame or glory will not help the person to build their self-esteem until they accept who they are internally. Psychological imbalances such as depression can distract the person from obtaining a higher level of self-esteem.

text: Most people have a need for stable self-respect and self-esteem. Maslow noted two versions of esteem needs: a "lower" version and a "higher" version. The "lower" version of esteem is the need for respect from others. This may include a need for status, recognition, fame, prestige, and attention. The "higher" version manifests itself as the need for self-respect. For example, the person may have a need for strength, competence[3], mastery, self-confidence, independence, and freedom. This "higher" version takes guidelines, the "hierarchies are interrelated rather than sharply separated".[5] This means that esteem and the subsequent levels are not strictly separated; instead, the levels are closely related.

text: Self-actualization[edit]

text: "What a man can be, he must be."[5]:91 This quotation forms the basis of the perceived need for self-actualization. This level of need refers to what a person's full potential is and the realization of that potential. Maslow describes this level as the desire to accomplish everything that one can, to become the most that one can be.[5]:92 Individuals may perceive or focus on this need very specifically. For example, one individual may have the strong desire to become an ideal parent. In another, the desire may be expressed athletically. For others, it may be expressed in paintings, pictures, or inventions.[5]:93 As previously mentioned, Maslow believed that to understand this level of need, the person must not only achieve the previous needs, but master them. Self-actualization can often be described as a value-based system when discussing its role in motivation. Since all levels of Maslow’s hierarchy must have been met in order to acquire this level, seeking to fulfill this form of satisfaction can be defined as an explicit motive. An explicit motive can be defined as a reward-based system that is used to intrinsically pursue certain values or goals.[3] Individuals who are motivated to pursue this level are seeking and understanding how their sense of self partakes within their human behavior. Self-actualization can include:[3]

text:
Mate Acquisition
Parenting
Utilizing Abilities
Utilizing Talents
Pursuing a goal
Seeking Happiness

text: Self-transcendence[edit]

text:
In his later years, Abraham Maslow explored a further dimension of motivation, while criticizing his original vision of self-actualization.[9][10][11][12] By this later theory, one finds fullest realization in giving oneself to something beyond oneself — for example, in altruism or spirituality. He equated this with the desire to reach the infinite.[13]
"Transcendence refers to the very highest and most inclusive or holistic levels of human consciousness, behaving and relating, as ends rather than means, to oneself, to significant others, to human beings in general, to other species, to nature, and to the cosmos" (Farther Reaches of Human Nature, New York 1971, p. 269).

text: Research[edit]

text: Recent research appears to validate the existence of universal human needs, although the hierarchy proposed by Maslow is called into question.[14][15]

text: Following World War II, the unmet needs of homeless and orphaned children presented difficulties that were often addressed with the help of attachment theory, which was initially based on Maslow and others'  developmental psychology work by John Bowlby.[16] Originally dealing primarily with maternal deprivation and concordant losses of essential and primal needs, attachment theory has since been extended to provide explanations of nearly all the human needs in Maslow's hierarchy, from sustenance and mating to group membership and justice.[17]

text: Criticism[edit]

text: Unlike most scientific theories, Maslow's hierarchy of needs has widespread influence outside academia. As Uriel Abulof argues, "The continued resonance of Maslow’s theory in popular imagination, however unscientific it may seem, is possibly the single most telling evidence of its significance: it explains human nature as something that most humans immediately recognize in themselves and others."[18] Still, academically, Maslow's theory is heavily contested.

text: Methodology[edit]

text:
Maslow studied what he called the master race of people such as Albert Einstein, Jane Addams, Eleanor Roosevelt, and Frederick Douglass rather than mentally ill or neurotic people, writing that "the study of crippled, stunted, immature, and unhealthy specimens can yield only a cripple psychology and a cripple philosophy."[5]:236
Maslow studied the healthiest 1% of the college student population.[19]

text: Ranking[edit]

text: Global ranking[edit]

text: In their extensive review of research based on Maslow's theory, Wahba and Bridwell found little evidence for the ranking of needs that Maslow described or for the existence of a definite hierarchy at all.[20]

text: The order in which the hierarchy is arranged has been criticized as being ethnocentric by Geert Hofstede.[21] Maslow's hierarchy of needs fails to illustrate and expand upon the difference between the social and intellectual needs of those raised in individualistic societies and those raised in collectivist societies. The needs and drives of those in individualistic societies tend to be more self-centered than those in collectivist societies, focusing on improvement of the self, with self-actualization being the apex of self-improvement. In collectivist societies, the needs of acceptance and community will outweigh the needs for freedom and individuality.[22]

text: Ranking of sex[edit]

text: The position and value of sex on the pyramid has also been a source of criticism regarding Maslow's hierarchy. Maslow's hierarchy places sex in the physiological needs category along with food and breathing; it lists sex solely from an individualistic perspective. For example, sex is placed with other physiological needs which must be satisfied before a person considers "higher" levels of motivation. Some critics feel this placement of sex neglects the emotional, familial, and evolutionary implications of sex within the community, although others point out that this is true of all of the basic needs.[23][24]

text: Changes to the hierarchy by circumstance[edit]

text: The higher-order (self-esteem and self-actualization) and lower-order (physiological, safety, and love) needs classification of Maslow's hierarchy of needs is not universal and may vary across cultures due to individual differences and availability of resources in the region or geopolitical entity/country.

text: In one study,[25] exploratory factor analysis (EFA) of a thirteen item scale showed there were two particularly important levels of needs in the US during the peacetime of 1993 to 1994: survival (physiological and safety) and psychological (love, self-esteem, and self-actualization). In 1991, a retrospective peacetime measure was established and collected during the Persian Gulf War and US citizens were asked to recall the importance of needs from the previous year. Once again, only two levels of needs were identified; therefore, people have the ability and competence to recall and estimate the importance of needs. For citizens in the Middle East (Egypt and Saudi Arabia), three levels of needs regarding importance and satisfaction surfaced during the 1990 retrospective peacetime. These three levels were completely different from those of the US citizens.

text: Changes regarding the importance and satisfaction of needs from the retrospective peacetime to the wartime due to stress varied significantly across cultures (the US vs. the Middle East). For the US citizens, there was only one level of needs since all needs were considered equally important. With regards to satisfaction of needs during the war, in the US there were three levels: physiological needs, safety needs, and psychological needs (social, self-esteem, and self-actualization). During the war, the satisfaction of physiological needs and safety needs were separated into two independent needs while during peacetime, they were combined as one. For the people of the Middle East, the satisfaction of needs changed from three levels to two during wartime.[26][27]

text: A 1981 study looked at how Maslow's hierarchy might vary across age groups.[28] A survey asked participants of varying ages to rate a set number of statements from most important to least important. The researchers found that children had higher physical need scores than the other groups, the love need emerged from childhood to young adulthood, the esteem need was highest among the adolescent group, young adults had the highest self-actualization level, and old age had the highest level of security, it was needed across all levels comparably. The authors argued that this suggested Maslow's hierarchy may be limited as a theory for developmental sequence since the sequence of the love need and the self-esteem need should be reversed according to age.

text: Definition of terms[edit]

text: Self-actualization[edit]

text: The term "self-actualization" may not universally convey Maslow's observations; this motivation refers to focusing on becoming the best person that one can possibly strive for in the service of both the self and others.[5][non-primary source needed] Maslow's term of self-actualization might not properly portray the full extent of this level; quite often, when a person is at the level of self-actualization, much of what they accomplish in general may benefit others, or "the greater good".[original research?]

text: Human or non-human needs[edit]

text: Abulof argues that  while Maslow stresses that “motivation theory must be anthropocentric rather than animalcentric,” his theory erects a largely animalistic pyramid, crowned with a human edge: “Man’s higher nature rests upon man’s lower nature, needing it as a foundation and collapsing without this foundation… Our godlike qualities rest upon and need our animal qualities.” Abulof notes that "all animals seek survival and safety, and many animals, especially mammals, also invest efforts to belong and gain esteem... The first four of Maslow’s classical five rungs feature nothing exceptionally human."[29] Even when it comes to "self-actualization," Abulof argues, it is unclear how distinctively human is the actualizing “self.” After all, the latter, according to Maslow, constitutes “an inner, more biological, more instinctoid core of human nature,” thus “the search for one’s own intrinsic, authentic values” checks the human freedom of choice: “A musician must make music," so freedom is limited to merely the choice of instrument.[30]


document_title: List of rivers by length

question_text: which is the longest rever in the world

question_tokens: which is the longest rever in the world

text: List of rivers by length

text: Definition of length

text: There are many factors, such as the source, the identification or the definition of the mouth, and the scale of measurement[1] of the river length between source and mouth, that determine the precise meaning of "river length". As a result, the length measurements of many rivers are only approximations (see also coastline paradox). In particular, there has long been disagreement as to whether the Nile or the Amazon is the world's longest river. The Nile has traditionally been considered longer, but in recent years some Brazilian and Peruvian studies have suggested that the Amazon is longer by measuring the river plus the adjacent Pará estuary and the longest connecting tidal canal.[2][3][4][5]

text: Even when detailed maps are available, the length measurement is not always clear. A river may have multiple channels, or anabranches. The length may depend on whether the center or the edge of the river is measured. It may not be clear how to measure the length through a lake. Seasonal and annual changes may alter both rivers and lakes. Other factors that can change the length of a river include cycles of erosion and flooding, dams, levees, and channelization. In addition, the length of meanders can change significantly over time due to natural or artificial cutoffs, when a new channel cuts across a narrow strip of land, bypassing a large river bend. For example, due to 18 cutoffs created between 1766 and 1885 the length of the Mississippi River from Cairo, Illinois, to New Orleans, Louisiana, was reduced by 218 miles (351 km).[6]

text: These points make it difficult, if not impossible, to get an accurate measurement of the length of a river. The varying accuracy and precision also makes it difficult to make length comparisons between different rivers without a degree of uncertainty.

text: List of rivers longer than 1000 km

text: This section possibly contains original research. Please improve it by verifying the claims made and adding inline citations. Statements consisting only of original research should be removed. (May 2017) (Learn how and when to remove this template message)

text: This article or section possibly contains synthesis of material which does not verifiably mention or relate to the main topic. Relevant discussion may be found on the talk page. (June 2009) (Learn how and when to remove this template message)

text: One should take the aforementioned discussion into account when using the data in the following table. For most rivers, different sources provide conflicting information on the length of a river system. The information in different sources is between parentheses.

text:
Continent color key

Africa
Asia
Australia
Europe
North America
South America

text:
River
Length (km)
Length (miles)
Drainage area
(km²)
[citation needed]
Average discharge
(m³/s)
[citation needed]
Outflow
Countries in the drainage basin[citation needed]

1.
Amazon–Ucayali–Tambo–Ene–Apurímac[n 1]
6,992
(6,400)
4,345
(3,976)
7,050,000
209,000
Atlantic Ocean
Brazil, Peru, Bolivia, Colombia, Ecuador, Venezuela, Guyana

2.
Nile–Kagera[n 1]
6,853
(6,650)
4,258
(4,132)
3,254,555
2,800
Mediterranean
Ethiopia, Eritrea, Sudan, Uganda, Tanzania, Kenya, Rwanda, Burundi, Egypt, Democratic Republic of the Congo, South Sudan

3.
Yangtze
(Chang Jiang; Long River)
6,300
(6,418)
3,917
(3,988)
1,800,000
31,900
East China Sea
China

4.
Mississippi–Missouri–Jefferson
6,275
3,902
2,980,000
16,200
Gulf of Mexico
United States (98.5%), Canada (1.5%)

5.
Yenisei–Angara–Selenge
5,539
3,445
2,580,000
19,600
Kara Sea
Russia (97%), Mongolia (2.9%)

6.
Yellow River
(Huang He)
5,464
3,395
745,000
2,110
Bohai Sea
China

7.
Ob–Irtysh
5,410
3,364
2,990,000
12,800
Gulf of Ob
Russia, Kazakhstan, China, Mongolia

8.
Paraná–Río de la Plata[8]
4,880
3,030
2,582,672
18,000
Río de la Plata
Brazil (46.7%), Argentina (27.7%), Paraguay (13.5%), Bolivia (8.3%), Uruguay (3.8%)

9.
Congo–Chambeshi
(Zaïre)
4,700
2,922
3,680,000
41,800
Atlantic Ocean
Democratic Republic of the Congo, Central African Republic, Angola, Republic of the Congo, Tanzania, Cameroon, Zambia, Burundi, Rwanda

10.
Amur–Argun
(Heilong Jiang)
4,444
2,763
1,855,000
11,400
Sea of Okhotsk
Russia, China, Mongolia

11.
Lena
4,400
2,736
2,490,000
17,100
Laptev Sea
Russia

12.
Mekong
(Lancang Jiang)
4,350
2,705
810,000
16,000
South China Sea
China, Myanmar, Laos, Thailand, Cambodia, Vietnam

13.
Mackenzie–Slave–Peace–Finlay
4,241
2,637
1,790,000
10,300
Beaufort Sea
Canada

14.
Niger
4,200
2,611
2,090,000
9,570
Gulf of Guinea
Nigeria (26.6%), Mali (25.6%), Niger (23.6%), Algeria (7.6%), Guinea (4.5%), Cameroon (4.2%), Burkina Faso (3.9%), Côte d'Ivoire, Benin, Chad

15.
Brahmaputra–Tsangpo
3,848
2,391
712,035
19,800[9][10]
Ganges
India (58.0%), China (19.7%), Nepal (9.0%), Bangladesh (6.6%), Disputed India/China (4.2%), Bhutan (2.4%)

16.
Murray–Darling
3,672[11]
2,282
1,061,000
767
Southern Ocean
Australia

17.
Tocantins–Araguaia
3,650
2,270
950,000
13,598
Atlantic Ocean, Amazon
Brazil

18.
Volga
3,645
2,266
1,380,000
8,080
Caspian Sea
Russia

19.
Indus–Sênggê Zangbo
3,610
2,250
960,000
7,160
Arabian Sea
Pakistan (93%), India, China, Kashmir (Disputed region between Pakistan, India and China)

20.
Shatt al-Arab–Euphrates
3,596
2,236
884,000
856
Persian Gulf
Iraq (60.5%), Turkey (24.8%), Syria (14.7%)

21.
Madeira–Mamoré–Grande–Caine–Rocha
3,380
2,100
1,485,200
31,200
Amazon
Brazil, Bolivia, Peru

22.
Purús
3,211
1,995
63,166
8,400
Amazon
Brazil, Peru

23.
Yukon
3,185
1,980[6]
850,000
6,210
Bering Sea
United States (59.8%), Canada (40.2%)

24.
São Francisco
3,180*
(2,900)
1,976*
(1,802)
610,000
3,300
Atlantic Ocean
Brazil

25.
Syr Darya–Naryn
3,078
1,913
219,000
703
Aral Sea
Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan

26.
Salween
(Nu Jiang)
3,060
1,901
324,000
3,153[12]
Andaman Sea
China (52.4%), Myanmar (43.9%), Thailand (3.7%)

27.
Saint Lawrence–Great Lakes
3,058
1,900[6]
1,030,000
10,100
Gulf of Saint Lawrence
Canada (52.1%), United States (47.9%)

28.
Rio Grande
3,057
1,900[6]
570,000
82
Gulf of Mexico
United States (52.1%), Mexico (47.9%)

29.
Lower Tunguska
2,989
1,857
473,000
3,600
Yenisei
Russia

30.
Danube–Breg (Donau, Dunăre, Duna, Dunav, Dunaj)
2,888*
1,795*
817,000
7,130
Black Sea
Romania (28.9%), Hungary (11.7%), Austria (10.3%), Serbia (10.3%), Germany (7.5%), Slovakia (5.8%), Bulgaria (5.2%), Croatia (4.5%),

31.
Zambezi
(Zambesi)
2,693*
1,673*
1,330,000
4,880
Mozambique Channel
Zambia (41.6%), Angola (18.4%), Zimbabwe (15.6%), Mozambique (11.8%), Malawi (8.0%), Tanzania (2.0%), Namibia, Botswana

32.
Vilyuy
2,650
1,647
454,000
1,480
Lena
Russia

33.
Araguaia
2,627
1,632
358,125
5,510
Tocantins
Brazil

34.
Ganges–Hooghly–Padma (Ganga)
2,620[13]
1,628
907,000
12,037[14]
Bay of Bengal
India, Bangladesh, Nepal, China

35.
Amu Darya–Panj
2,620
1,628
534,739
1,400
Aral Sea
Uzbekistan, Turkmenistan, Tajikistan, Afghanistan

36.
Japurá (Rio Yapurá)
2,615*
1,625*
242,259
6,000
Amazon
Brazil, Colombia

37.
Nelson–Saskatchewan
2,570
1,597
1,093,000
2,575
Hudson Bay
Canada, United States

38.
Paraguay (Rio Paraguay)
2,549
1,584
900,000
4,300
Paraná
Brazil, Paraguay, Bolivia, Argentina

39.
Kolyma
2,513
1,562
644,000
3,800
East Siberian Sea
Russia

40.
Pilcomayo
2,500
1,553
270,000

Paraguay
Paraguay, Argentina, Bolivia

41.
Upper Ob–Katun
2,490
1,547

Ob
Russia

42.
Ishim
2,450
1,522
177,000
56
Irtysh
Kazakhstan, Russia

43.
Juruá
2,410
1,498
200,000
6,000
Amazon
Peru, Brazil

44.
Ural
2,428
1,509
237,000
475
Caspian Sea
Russia, Kazakhstan

45.
Arkansas
2,348
1,459
505,000
(435,122)
1,066
Mississippi
United States

46.
Colorado (western U.S.)
2,333
1,450
390,000
1,200
Gulf of California
United States, Mexico

47.
Olenyok
2,292
1,424
219,000
1,210
Laptev Sea
Russia

48.
Dnieper
2,287
1,421
516,300
1,670
Black Sea
Russia, Belarus, Ukraine

49.
Aldan
2,273
1,412
729,000
5,060
Lena
Russia

50.
Ubangi–Uele[15]
2,270
1,410
772,800
4,000
Congo
Democratic Republic of the Congo, Central African Republic, Republic of Congo

51.
Negro
2,250
1,398
720,114
26,700
Amazon
Brazil, Venezuela, Colombia

52.
Columbia
2,250 (1,953)
1,398 (1,214)
415,211
7,500
Pacific Ocean
United States, Canada

53.
Pearl–Zhu Jiang
2,200
1,376
437,000
13,600
South China Sea
China (98.5%), Vietnam (1.5%)

54.
Red (USA)
2,188
1,360
78,592
875
Mississippi
United States

55.
Ayeyarwady
(Irrawaddy)
2,170
1,348
411,000
13,000
Andaman Sea
Myanmar, China

56.
Kasai
2,153
1,338
880,200
10,000
Congo
Angola, Democratic Republic of the Congo

57.
Ohio–Allegheny
2,102
1,306
490,603
7,957
Mississippi
United States

58.
Orinoco
2,101
1,306
1,380,000
33,000
Atlantic Ocean
Venezuela, Colombia, Guyana

59.
Tarim
2,100
1,305
557,000

Lop Nur
P. R. China

60.
Xingu
2,100
1,305

Amazon
Brazil

61.
Orange
2,092
1,300

Atlantic Ocean
South Africa, Namibia, Botswana, Lesotho

62.
Northern Salado
2,010
1,249

Paraná
Argentina

63.
Vitim
1,978
1,229

Lena
Russia

64.
Tigris
1,950
1,212

Shatt al-Arab
Turkey, Iraq, Syria

65.
Songhua
1,927
1,197

Amur
P. R. China

66.
Tapajós
1,900
1,181

Amazon
Brazil

67.
Don
1,870
1,162
425,600
935
Sea of Azov
Russia, Ukraine

68.
Stony Tunguska
1,865
1,159
240,000

Yenisei
Russia

69.
Pechora
1,809
1,124
322,000
4,100
Barents Sea
Russia

70.
Kama
1,805
1,122
507,000
4,100
Volga
Russia

71.
Limpopo
1,800
1,118
413,000

Indian Ocean
Mozambique, Zimbabwe, South Africa, Botswana

72.
Chulym
1,799
1,118
134,000

Ob
Russia

73.
Guaporé (Itenez)
1,749
1,087

Mamoré
Brazil, Bolivia

74.
Indigirka
1,726
1,072
360,400
1,810
East Siberian Sea
Russia

75.
Snake
1,670
1,038
279,719
1,611
Columbia
United States

76.
Senegal
1,641
1,020
419,659

Atlantic Ocean
Guinea, Senegal, Mali, Mauritania

77.
Uruguay
1,610
1,000
370,000

Atlantic Ocean
Uruguay, Argentina, Brazil

78.
Blue Nile
1,600
994
326,400

Nile
Ethiopia, Sudan

78.
Churchill
1,600
994

Hudson Bay
Canada

78.
Khatanga
1,600
994

Laptev Sea
Russia

78.
Okavango
1,600
994

Okavango Delta
Namibia, Angola, Botswana

78.
Volta
1,600
994

Gulf of Guinea
Ghana, Burkina Faso, Togo, Côte d'Ivoire, Benin

83.
Beni
1,599
994
283,350
8,900
Madeira
Bolivia

84.
Platte
1,594
990

Missouri
United States

85.
Tobol
1,591
989

Irtysh
Kazakhstan, Russia

86.
Jubba–Shebelle
1,580*
982*

Indian Ocean
Ethiopia, Somalia

87.
Içá (Putumayo)
1,575
979

Amazon
Brazil, Peru, Colombia, Ecuador

88.
Magdalena
1,550
963
263,858
9,000
Caribbean
Colombia

89.
Han
1,532
952

Yangtze
P. R. China

89.
Kura/Mt'k'vari
1,515
941
188,400
575
Caspian Sea
Turkey, Georgia, Azerbaijan

91.
Oka
1,500
932
245,000
1,258
Volga
Russia

92.
Guaviare
1,497
930

Orinoco
Colombia

93.
Pecos
1,490
926

Rio Grande
United States

94.
Murrumbidgee River
1,485 [16]
923
84,917
120
Murray River
Australia

95.
Upper Yenisei–Little Yenisei (Kaa-Hem)
1,480
920

Yenisei
Russia, Mongolia

96.
Godavari
1,465
910
312,812
3,061
Bay of Bengal
India

97.
Colorado (Texas)
1,438
894

Gulf of Mexico
United States

97.
Río Grande (Guapay)
1,438
894
102,600
264
Ichilo
Bolivia

99.
Belaya
1,420
882
142,000
858
Kama
Russia

99.
Cooper–Barcoo
1,420
880

Lake Eyre
Australia

101.
Marañón
1,415
879

Amazon
Peru

102.
Dniester
1,411 (1,352)
877 (840)
72,100
310
Black Sea
Ukraine, Moldova

103.
Benue
1,400
870

Niger
Cameroon, Nigeria

103.
Ili (Yili)
1,400
870

Lake Balkhash
P. R. China, Kazakhstan

103.
Warburton–Georgina
1,400
870
365,000

Lake Eyre
Australia

106.
Sutlej
1,372
852

Chenab
China, India, Pakistan

107.
Yamuna
1,370
851
366,223
2,950
Ganges
India

107.
Vyatka
1,370
851
129,000
890
Kama
Russia

109.
Fraser
1,368
850
220,000
3,475
Pacific Ocean
Canada

110.
Grande
1,360
845

Paraná
Brazil

111.
Brazos
1,352
840

Gulf of Mexico
United States

112.
Liao
1,345
836

Bohai Sea
P. R. China

113.
Lachlan River
1,338 [16]
831
84,700
49
Murrumbidgee River
Australia

114.
Yalong
1,323
822

Yangtze
P. R. China

115.
Iguaçu
1,320
820

Paraná
Brazil, Argentina

116.
Olyokma
1,320
820

Lena
Russia

117.
Northern Dvina–Sukhona
1,302
809
357,052
3,332
White Sea
Russia

118.
Krishna
1,300
808

Bay of Bengal
India

118.
Iriri
1,300
808

Xingu
Brazil

120.
Narmada
1,289
801

Arabian Sea
India

121.
Lomami[17]
1,280
795

Congo
Democratic Republic of the Congo

122.
Ottawa
1,271
790
146,300
1,950
Saint Lawrence
Canada

123.
Lerma–Rio Grande de Santiago
1,270
789
119,543

Pacific Ocean
Mexico

124.
Elbe–Vltava
1,252
778
148,268
711
North Sea
Germany, Czech Republic

125.
Zeya
1,242
772

Amur
Russia

126.
Juruena
1,240
771

Tapajós
Brazil

127.
Upper Mississippi
1,236
768

Mississippi
United States

128.
Rhine
1,233
768
185,000 [18]
2,330
North Sea
Germany (57.3%), Switzerland (15.1%), Netherlands (12.3%), France (12.2%), Luxembourg (1.4%), Austria (1.3%), Belgium (0.4%), Liechtenstein (0.1%), Italy (0.03%)

129.
Athabasca
1,231
765
95,300

Mackenzie
Canada

130.
Canadian
1,223
760

Arkansas
United States

131.
North Saskatchewan
1,220
758

Saskatchewan
Canada

132.
Vistula-Bug
1,213
754
194,424
1,080
Baltic Sea
Poland, Belarus, Ukraine

133.
Vaal
1,210
752

Orange
South Africa

134.
Shire
1,200
746

Zambezi
Mozambique, Malawi

135.
Nen
(Nonni)
1,190
739

Songhua
P. R. China

136.
Kızıl River
1,182
734
115,000
400
Black Sea
Turkey

137.
Green
1,175
730

Colorado (western U.S.)
United States

138.
Milk
1,173
729

Missouri
United States, Canada

139.
Chindwin
1,158
720

Ayeyarwady
Myanmar

140.
Sankuru
1,150
715

Kasai
Democratic Republic of the Congo

141.
Wu
1,150
715
80,300
1,108
Yangtze
China

142.
Red (Asia)
1,149
714
143,700
2,640
Gulf of Tonkin
China, Vietnam

143.
James (Dakotas)
1,143
710

Missouri
United States

144.
Kapuas
1,143
710

South China Sea
Indonesia

145.
Desna
1,130
702
88,900
360
Dnieper
Russia, Belarus, Ukraine

146.
Helmand
1,130
702

Hamun-i-Helmand
Afghanistan, Iran

147.
Madre de Dios
1,130
702
125,000
4,915
Beni
Peru, Bolivia

148.
Tietê
1,130
702

Paraná
Brazil

149.
Vychegda
1,130
702
121,000
1160
Northern Dvina
Russia

150.
Sepik
1,126
700
77,700

Pacific Ocean
Papua New Guinea, Indonesia

151.
Cimarron
1,123
698

Arkansas
United States

152.
Anadyr
1,120
696

Gulf of Anadyr
Russia

152.
Paraíba do Sul
1,120
696

Atlantic Ocean
Brazil

154.
Jialing River
1,119
695

Yangtze
P. R. China

155.
Liard
1,115
693

Mackenzie
Canada

156.
Cumberland
1,105
687
46,830
862
Mississippi
United States

157.
White
1,102
685

Mississippi
United States

158.
Huallaga
1,100
684

Marañón
Peru

158.
Kwango
1,100
684
263,500
2,700
Kasai
Angola, Democratic Republic of the Congo

158.
Draa
1,100
684

Atlantic Ocean
Morocco

161.
Gambia
1,094
680

Atlantic Ocean
The Gambia, Senegal, Guinea

162.
Chenab
1,086
675

Indus
India, Pakistan

163.
Yellowstone
1,080
671
114,260

Missouri
United States

163.
Ghaghara
1,080
671
127,950
2,990
Ganges
India, Nepal, China

165.
Huai River
1,078
670
270,000
1,110
Yangtze
China

166.
Aras
1,072
665
102,000
285
Kura
Turkey, Armenia, Azerbaijan, Iran

167.
Chu River
1,067
663
62,500

none
Kyrgyzstan, Kazakhstan

168.
Seversky Donets
1,078 (1,053)
670 (654)
98,900
159
Don
Russia, Ukraine

169.
Bermejo
1,050
652

Paraguay
Argentina, Bolivia

169.
Fly
1,050
652

Gulf of Papua
Papua New Guinea, Indonesia

169.
Kuskokwim
1,050
652

Bering Sea
United States

172.
Tennessee
1,049
652

Ohio
United States

173.
Oder–Warta
1,045
649
118,861
550
Baltic Sea
Poland, Germany, Czech Republic

174.
Aruwimi[17]
1,030
640

Congo
Democratic Republic of the Congo

175.
Daugava
1,020
634
87,900
678
Gulf of Riga
Latvia, Belarus, Russia

176.
Gila
1,015
631

Colorado (western U.S.)
United States

177.
Loire
1,012
629
115,271
840
Atlantic Ocean
France

178.
Essequibo
1,010
628

Atlantic Ocean
Guyana

178.
Khoper
1,010
628
61,100
150
Don
Russia

180.
Tagus
(Tajo/Tejo)
1,006
625
80,100
444
Atlantic Ocean
Spain, Portugal

181.
Flinders River
1,004 [16]
624
109,000
122
Gulf of Carpentaria
Australia

text: Notes

text:
When the length of a river is followed by an asterisk, it is an average of multiple information sources. If the difference in lengths between given information sources is significant, all lengths are listed. But if the lengths from secondary information sources are similar, they are averaged and that figure has an asterisk.
Scientists debate whether the Amazon or the Nile is the longest river in the world. Traditionally, the Nile is considered longer, but recent information suggests that the Amazon may be longer. Differences in the recorded length of the Amazon mainly depend on whether or not it is valid to take a course south of the Ilha de Marajó at the Amazon's mouth. New evidence, (dated 16 June 2007) obtained from a high-altitude scientific venture in the Andes, claims that "the Amazon is longer than the Nile by 100km, with its longest headwater being the Carhuasanta stream originating in the south of Peru on the Nevado Mismi mountain's northern slopes and flowing into the Río Apurímac".[19] However, the origin of the river at Nevado Mismi had already been known more than one decade earlier (see Jacek Palkiewicz), and satellite based measuring from this origin to the Amazon mouth has resulted in not more than 6,400 km.
Generally, the most commonly used/anglicised name of the river is used. The name in a native language or alternate spelling may be shown.

text:
The Mississippi River just north of St. Louis.

Saint Lawrence River along the New York-Quebec border.

text: River systems that may have existed in the past

text: Amazon-Congo

text: The Amazon basin formerly drained westwards into the Pacific Ocean, until the Andes rose and reversed the drainage.[20]

text: The Congo basin is completely surrounded by high land, except for its long narrow exit valley past Kinshasa, including waterfalls around Manyanga. That gives the impression that most of the Congo basin was formerly on a much higher land level and that it was rejuvenated by much of its lower course being removed. Before Gondwanaland broke up due to continental drift, the Congo would likely have flowed into the Amazon.[citation needed]

text: West Siberian Glacial Lake drainage

text: This river would have been about 10,000 km (6,200 mi) long, in the last Ice Age. Its longest headwater was the Selenga river of Mongolia: it drained through ice-dammed lakes and the Aral Sea and the Caspian Sea to the Black Sea.

text: Lobourg

text: During the last glacial maximum, much of what is now the southern part of the North Sea was land, known to archaeologists as Doggerland. At this time, the Thames, the Meuse, the Scheldt, and the Rhine probably joined before flowing into the sea, in a system known by palaeogeographers as the Loubourg or Lobourg River System.[21] There is some debate as to whether this river would have flowed southwest into what is now the English Channel, or flowed north, emerging into the North Sea close to modern Yorkshire. If the latter hypothesis is true, the Rhine would have attained a length of close to 1,650 kilometres (1,030 mi). The former hypothesis would have produced a shorter river, some 1,400 kilometres (870 mi) in length. Current scientific research favours the former opinion, with the Thames and Rhine meeting in a large lake, the outflow of which was close to the present-day Straits of Dover.[22]


document_title: List of countries by military expenditures

question_text: us military budget as a percentage of gdp

text: List[edit]

text:
List by the Stockholm International Peace Research Institute2018 Fact Sheet (for 2017)[1]SIPRI Military Expenditure Database[3]

List by the International Institute for Strategic StudiesTop 15 Defence Budgets 2017[4]

Rank
Country
Spending  ($ Bn.)
% of GDP

% of World share

World total
1,739
2.2

01
 United States
610.0
3.1

35.0

02
 People's Republic of China[a]
228.0
1.9

13.0

03
 Saudi Arabia[a][b]
69.4
10

4.0

04
 Russia
66.3
4.3

3.8

05
 India
63.9
2.5

3.7

06
 France
57.8
2.3

3.3

07
 United Kingdom
47.2
1.8

2.7

08
 Japan
45.4
0.9

2.6

09
 Germany
44.3
1.2

2.5

10
 South Korea
39.2
2.6

2.3

11
 Brazil
29.3
1.4

1.7

12
 Italy
29.2
1.7

1.7

13
 Australia
27.5
2.0

1.6

14
 Canada
20.6
1.3

1.2

15
 Turkey
18.2
2.2

1.0

Rank
Country
Spending  ($ Bn.)

01
 United States
602.8

02
 People's Republic of China
150.5

03
 Saudi Arabia
76.7

04
 Russia
61.2

05
 India
52.5

06
 United Kingdom
50.7

07
 France
48.6

08
 Japan
46.0

09
 Germany
41.7

10
 South Korea
35.7

11
 Brazil
29.4

12
 Australia
25.0

13
 Italy
22.9

14
 Israel
21.6

15
 Iraq
19.4

text: Notes[edit]

text:
^ Jump up to: a b SIPRI estimate.

Jump up ^ The figures for Saudi Arabia include expenditure for public order and safety and might be slightly overestimated.