Welch's t-test

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In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the hypothesis that two populations have equal means. Welch's t-test is an adaptation of Student's t-test,[1] that is, it has been derived with the help of Student's t-test and is more reliable when the two samples have unequal variances and unequal sample sizes.[2] These tests are often referred to as "unpaired" or "independent samples" t-tests, as they are typically applied when the statistical units underlying the two samples being compared are non-overlapping. Given that Welch's t-test has been less popular than Student's t-test[2] and may be less familiar to readers, a more informative name is "Welch's unequal variances t-test" or "unequal variances t-test" for brevity.

Assumptions[edit]

Student's t-test assumes that the two populations have normal distributions and with equal variances. Welch's t-test is designed for unequal variances, but the assumption of normality is maintained.[1] Welch's t-test is an approximate solution to the Behrens–Fisher problem.

Calculations[edit]

Welch's t-test defines the statistic t by the following formula:

where , and are the 1st sample mean, sample variance and sample size, respectively. Unlike in Student's t-test, the denominator is not based on a pooled variance estimate.

The degrees of freedom   associated with this variance estimate is approximated using the Welch–Satterthwaite equation:

Here , the degrees of freedom associated with the first variance estimate. , the degrees of freedom associated with the 2nd variance estimate.

Welch's t-test can also be calculated for ranked data and might then be named Welch's U-test.[3]

Statistical test[edit]

Once t and have been computed, these statistics can be used with the t-distribution to test the null hypothesis that the two population means are equal (using a two-tailed test), or the alternative hypothesis that one of the population means is greater than or equal to the other (using a one-tailed test). The approximate degrees of freedom is rounded down to the nearest integer.

Advantages and limitations[edit]

Welch's t-test is more robust than Student's t-test and maintains type I error rates close to nominal for unequal variances and for unequal sample sizes. Furthermore, the power of Welch's t-test comes close to that of Student's t-test, even when the population variances are equal and sample sizes are balanced.[2] Welch's t-test can be generalized to more than 2-samples [4], which is more robust than one-way analysis of variance (ANOVA).

It is not recommended to pre-test for equal variances and then choose between Student's t-test or Welch's t-test.[5] Rather, Welch's t-test can be applied directly and without any substantial disadvantages to Student's t-test as noted above. Welch's t-test remains robust for skewed distributions and large sample sizes.[6] Reliability decreases for skewed distributions and smaller samples, where one could possibly perform Welch's t-test on ranked data.[3]

Examples[edit]

The following three examples compare Welch's t-test and Student's t-test. Samples are from random normal distributions using the R programming language.

For all three examples, the population means were and .

The first example is for equal variances () and equal sample sizes (). Let A1 and A2 denote two random samples:

The second example is for unequal variances (, ) and unequal sample sizes (, ). The smaller sample has the larger variance:

The third example is for unequal variances (, ) and unequal sample sizes (, ). The larger sample has the larger variance:

Reference p-values were obtained by simulating the distributions of the t statistics for the null hypothesis of equal population means (). Results are summarised in the table below, with two-tailed p-values:

Sample A1 Sample A2 Student's t-test Welch's t-test
Example
1 15 20.8 7.9 15 23.0 3.8 −2.46 28 0.021 0.021 −2.46 25.0 0.021 0.017
2 10 20.6 9.0 20 22.1 0.9 −2.10 28 0.045 0.150 −1.57 9.9 0.149 0.144
3 10 19.4 1.4 20 21.6 17.1 −1.64 28 0.110 0.036 −2.22 24.5 0.036 0.042

Welch's t-test and Student's t-test gave practically identical results for the two samples with equal variances and equal sample sizes (Example 1). For unequal variances, Student's t-test gave a low p-value when the smaller sample had a larger variance (Example 2) and a high p-value when the larger sample had a larger variance (Example 3). For unequal variances, Welch's t-test gave p-values close to simulated p-values.

Software implementations[edit]

Language/Program Function Notes
LibreOffice TTEST(Data1; Data2; Mode; Type) See [1]
MATLAB ttest2(data1, data2, 'Vartype', 'unequal') See [2]
Microsoft Excel pre 2010 TTEST(array1, array2, tails, type) See [3]
Microsoft Excel 2010 and later T.TEST(array1, array2, tails, type) See [4]
Python scipy.stats.ttest_ind(a, b, axis=0, equal_var=False) See [5]
R t.test(data1, data2, alternative="two.sided", var.equal=FALSE) See [6]
Julia UnequalVarianceTTest(data1, data2) See [7]
Stata ttest varname1 == varname2, welch See 8

See also[edit]

References[edit]

  1. ^ a b Welch, B. L. (1947). "The generalization of "Student's" problem when several different population variances are involved". Biometrika. 34 (1–2): 28–35. doi:10.1093/biomet/34.1-2.28. MR 19277. 
  2. ^ a b c Ruxton, G. D. (2006). "The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test". Behavioral Ecology. 17: 688–690. doi:10.1093/beheco/ark016. 
  3. ^ a b Fagerland, M. W.; Sandvik, L. (2009). "Performance of five two-sample location tests for skewed distributions with unequal variances". Contemporary Clinical Trials. 30: 490–496. doi:10.1016/j.cct.2009.06.007. 
  4. ^ Welch, B. L. (1951). "On the Comparison of Several Mean Values: An Alternative Approach". Biometrika. 38: 330–336. doi:10.2307/2332579. Retrieved 26 September 2016. 
  5. ^ Zimmerman, D. W. (2004). "A note on preliminary tests of equality of variances". British Journal of Mathematical and Statistical Psychology. 57: 173–181. doi:10.1348/000711004849222. 
  6. ^ Fagerland, M. W. (2012). "t-tests, non-parametric tests, and large studies—a paradox of statistical practice?". BioMed Central Medical Research Methodology. 12: 78. doi:10.1186/1471-2288-12-78.