pingouin.ttest#

pingouin.ttest(x, y, paired=False, alternative='two-sided', correction='auto', r=0.707, confidence=0.95)[source]#

T-test.

Parameters:
xarray_like

First set of observations.

yarray_like or float

Second set of observations. If y is a single value, a one-sample T-test is computed against that value (= “mu” in the t.test R function).

pairedboolean

Specify whether the two observations are related (i.e. repeated measures) or independent.

alternativestring

Defines the alternative hypothesis, or tail of the test. Must be one of “two-sided” (default), “greater” or “less”. Both “greater” and “less” return one-sided p-values. “greater” tests against the alternative hypothesis that the mean of x is greater than the mean of y.

correctionstring or boolean

For unpaired two sample T-tests, specify whether or not to correct for unequal variances using Welch separate variances T-test. If ‘auto’, it will automatically uses Welch T-test when the sample sizes are unequal, as recommended by Zimmerman 2004.

rfloat

Cauchy scale factor for computing the Bayes Factor. Smaller values of r (e.g. 0.5), may be appropriate when small effect sizes are expected a priori; larger values of r are appropriate when large effect sizes are expected (Rouder et al 2009). The default is 0.707 (= \(\sqrt{2} / 2\)).

confidencefloat

Confidence level for the confidence intervals (0.95 = 95%)

Added in version 0.3.9.

Returns:
statspandas.DataFrame

  • 'T': T-value

  • 'dof': degrees of freedom

  • 'alternative': alternative of the test

  • 'p-val': p-value

  • 'CI95%': confidence intervals of the difference in means

  • 'cohen-d': Cohen d effect size

  • 'BF10': Bayes Factor of the alternative hypothesis

  • 'power': achieved power of the test ( = 1 - type II error)

See also

mwu, wilcoxon, anova, rm_anova, pairwise_tests, compute_effsize

Notes

Missing values are automatically removed from the data. If x and y are paired, the entire row is removed (= listwise deletion).

The T-value for unpaired samples is defined as:

\[t = \frac{\overline{x} - \overline{y}} {\sqrt{\frac{s^{2}_{x}}{n_{x}} + \frac{s^{2}_{y}}{n_{y}}}}\]

where \(\overline{x}\) and \(\overline{y}\) are the sample means, \(n_{x}\) and \(n_{y}\) are the sample sizes, and \(s^{2}_{x}\) and \(s^{2}_{y}\) are the sample variances. The degrees of freedom \(v\) are \(n_x + n_y - 2\) when the sample sizes are equal. When the sample sizes are unequal or when correction=True, the Welch–Satterthwaite equation is used to approximate the adjusted degrees of freedom:

\[v = \frac{(\frac{s^{2}_{x}}{n_{x}} + \frac{s^{2}_{y}}{n_{y}})^{2}} {\frac{(\frac{s^{2}_{x}}{n_{x}})^{2}}{(n_{x}-1)} + \frac{(\frac{s^{2}_{y}}{n_{y}})^{2}}{(n_{y}-1)}}\]

The p-value is then calculated using a T distribution with \(v\) degrees of freedom.

The T-value for paired samples is defined by:

\[t = \frac{\overline{x}_d}{s_{\overline{x}}}\]

where

\[s_{\overline{x}} = \frac{s_d}{\sqrt n}\]

where \(\overline{x}_d\) is the sample mean of the differences between the two paired samples, \(n\) is the number of observations (sample size), \(s_d\) is the sample standard deviation of the differences and \(s_{\overline{x}}\) is the estimated standard error of the mean of the differences. The p-value is then calculated using a T-distribution with \(n-1\) degrees of freedom.

The scaled Jeffrey-Zellner-Siow (JZS) Bayes Factor is approximated using the pingouin.bayesfactor_ttest() function.

Results have been tested against JASP and the t.test R function.

References

  • https://www.itl.nist.gov/div898/handbook/eda/section3/eda353.htm

  • Delacre, M., Lakens, D., & Leys, C. (2017). Why psychologists should by default use Welch’s t-test instead of Student’s t-test. International Review of Social Psychology, 30(1).

  • Zimmerman, D. W. (2004). A note on preliminary tests of equality of variances. British Journal of Mathematical and Statistical Psychology, 57(1), 173-181.

  • Rouder, J.N., Speckman, P.L., Sun, D., Morey, R.D., Iverson, G., 2009. Bayesian t tests for accepting and rejecting the null hypothesis. Psychon. Bull. Rev. 16, 225–237. https://doi.org/10.3758/PBR.16.2.225

Examples

  1. One-sample T-test.

>>> from pingouin import ttest
>>> x = [5.5, 2.4, 6.8, 9.6, 4.2]
>>> ttest(x, 4).round(2)
          T  dof alternative  p-val         CI95%  cohen-d   BF10  power
T-test  1.4    4   two-sided   0.23  [2.32, 9.08]     0.62  0.766   0.19

  1. One sided paired T-test.

>>> pre = [5.5, 2.4, 6.8, 9.6, 4.2]
>>> post = [6.4, 3.4, 6.4, 11., 4.8]
>>> ttest(pre, post, paired=True, alternative='less').round(2)
           T  dof alternative  p-val          CI95%  cohen-d   BF10  power
T-test -2.31    4        less   0.04  [-inf, -0.05]     0.25  3.122   0.12

Now testing the opposite alternative hypothesis

>>> ttest(pre, post, paired=True, alternative='greater').round(2)
           T  dof alternative  p-val         CI95%  cohen-d  BF10  power
T-test -2.31    4     greater   0.96  [-1.35, inf]     0.25  0.32   0.02

  1. Paired T-test with missing values.

>>> import numpy as np
>>> pre = [5.5, 2.4, np.nan, 9.6, 4.2]
>>> post = [6.4, 3.4, 6.4, 11., 4.8]
>>> ttest(pre, post, paired=True).round(3)
            T  dof alternative  p-val          CI95%  cohen-d   BF10  power
T-test -5.902    3   two-sided   0.01  [-1.5, -0.45]    0.306  7.169  0.073

Compare with SciPy

>>> from scipy.stats import ttest_rel
>>> np.round(ttest_rel(pre, post, nan_policy="omit"), 3)
array([-5.902,  0.01 ])

  1. Independent two-sample T-test with equal sample size.

>>> np.random.seed(123)
>>> x = np.random.normal(loc=7, size=20)
>>> y = np.random.normal(loc=4, size=20)
>>> ttest(x, y)
               T  dof alternative         p-val         CI95%   cohen-d       BF10  power
T-test  9.106452   38   two-sided  4.306971e-11  [2.64, 4.15]  2.879713  1.366e+08    1.0

  1. Independent two-sample T-test with unequal sample size. A Welch’s T-test is used.

>>> np.random.seed(123)
>>> y = np.random.normal(loc=6.5, size=15)
>>> ttest(x, y)
               T        dof alternative     p-val          CI95%   cohen-d   BF10     power
T-test  1.996537  31.567592   two-sided  0.054561  [-0.02, 1.65]  0.673518  1.469  0.481867

  1. However, the Welch’s correction can be disabled:

>>> ttest(x, y, correction=False)
               T  dof alternative     p-val          CI95%   cohen-d   BF10     power
T-test  1.971859   33   two-sided  0.057056  [-0.03, 1.66]  0.673518  1.418  0.481867

Compare with SciPy

>>> from scipy.stats import ttest_ind
>>> np.round(ttest_ind(x, y, equal_var=True), 6)  # T value and p-value
array([1.971859, 0.057056])