scipy.stats.ttest_ind(a, b, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0)[source]

Calculate the T-test for the means of two independent samples of scores.

This is a test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes that the populations have identical variances by default.

a, barray_like

The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).

axisint or None, optional

Axis along which to compute test. If None, compute over the whole arrays, a, and b.

equal_varbool, optional

If True (default), perform a standard independent 2 sample test that assumes equal population variances [1]. If False, perform Welch’s t-test, which does not assume equal population variance [2].

New in version 0.11.0.

nan_policy{‘propagate’, ‘raise’, ‘omit’}, optional

Defines how to handle when input contains nan. The following options are available (default is ‘propagate’):

  • ‘propagate’: returns nan

  • ‘raise’: throws an error

  • ‘omit’: performs the calculations ignoring nan values

The ‘omit’ option is not currently available for permutation tests or one-sided asympyotic tests.

permutationsnon-negative int, np.inf, or None (default), optional

If 0 or None (default), use the t-distribution to calculate p-values. Otherwise, permutations is the number of random permutations that will be used to estimate p-values using a permutation test. If permutations equals or exceeds the number of distinct partitions of the pooled data, an exact test is performed instead (i.e. each distinct partition is used exactly once). See Notes for details.

New in version 1.7.0.

random_state{None, int, numpy.random.Generator,

If seed is None (or np.random), the numpy.random.RandomState singleton is used. If seed is an int, a new RandomState instance is used, seeded with seed. If seed is already a Generator or RandomState instance then that instance is used.

Pseudorandom number generator state used to generate permutations (used only when permutations is not None).

New in version 1.7.0.

alternative{‘two-sided’, ‘less’, ‘greater’}, optional

Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):

  • ‘two-sided’: the means of the distributions underlying the samples are unequal.

  • ‘less’: the mean of the distribution underlying the first sample is less than the mean of the distribution underlying the second sample.

  • ‘greater’: the mean of the distribution underlying the first sample is greater than the mean of the distribution underlying the second sample.

New in version 1.6.0.

trimfloat, optional

If nonzero, performs a trimmed (Yuen’s) t-test. Defines the fraction of elements to be trimmed from each end of the input samples. If 0 (default), no elements will be trimmed from either side. The number of trimmed elements from each tail is the floor of the trim times the number of elements. Valid range is [0, .5).

New in version 1.7.

statisticfloat or array

The calculated t-statistic.

pvaluefloat or array

The p-value.


Suppose we observe two independent samples, e.g. flower petal lengths, and we are considering whether the two samples were drawn from the same population (e.g. the same species of flower or two species with similar petal characteristics) or two different populations.

The t-test quantifies the difference between the arithmetic means of the two samples. The p-value quantifies the probability of observing as or more extreme values assuming the null hypothesis, that the samples are drawn from populations with the same population means, is true. A p-value larger than a chosen threshold (e.g. 5% or 1%) indicates that our observation is not so unlikely to have occurred by chance. Therefore, we do not reject the null hypothesis of equal population means. If the p-value is smaller than our threshold, then we have evidence against the null hypothesis of equal population means.

By default, the p-value is determined by comparing the t-statistic of the observed data against a theoretical t-distribution. When 1 < permutations < binom(n, k), where

  • k is the number of observations in a,

  • n is the total number of observations in a and b, and

  • binom(n, k) is the binomial coefficient (n choose k),

the data are pooled (concatenated), randomly assigned to either group a or b, and the t-statistic is calculated. This process is performed repeatedly (permutation times), generating a distribution of the t-statistic under the null hypothesis, and the t-statistic of the observed data is compared to this distribution to determine the p-value. When permutations >= binom(n, k), an exact test is performed: the data are partitioned between the groups in each distinct way exactly once.

The permutation test can be computationally expensive and not necessarily more accurate than the analytical test, but it does not make strong assumptions about the shape of the underlying distribution.

Use of trimming is commonly referred to as the trimmed t-test. At times called Yuen’s t-test, this is an extension of Welch’s t-test, with the difference being the use of winsorized means in calculation of the variance and the trimmed sample size in calculation of the statistic. Trimming is reccomended if the underlying distribution is long-tailed or contaminated with outliers [4].






Yuen, Karen K. “The Two-Sample Trimmed t for Unequal Population Variances.” Biometrika, vol. 61, no. 1, 1974, pp. 165-170. JSTOR, Accessed 30 Mar. 2021.


Yuen, Karen K., and W. J. Dixon. “The Approximate Behaviour and Performance of the Two-Sample Trimmed t.” Biometrika, vol. 60, no. 2, 1973, pp. 369-374. JSTOR, Accessed 30 Mar. 2021.


>>> from scipy import stats
>>> rng = np.random.default_rng()

Test with sample with identical means:

>>> rvs1 = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
>>> rvs2 = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
>>> stats.ttest_ind(rvs1, rvs2)
Ttest_indResult(statistic=-0.4390847099199348, pvalue=0.6606952038870015)
>>> stats.ttest_ind(rvs1, rvs2, equal_var=False)
Ttest_indResult(statistic=-0.4390847099199348, pvalue=0.6606952553131064)

ttest_ind underestimates p for unequal variances:

>>> rvs3 = stats.norm.rvs(loc=5, scale=20, size=500, random_state=rng)
>>> stats.ttest_ind(rvs1, rvs3)
Ttest_indResult(statistic=-1.6370984482905417, pvalue=0.1019251574705033)
>>> stats.ttest_ind(rvs1, rvs3, equal_var=False)
Ttest_indResult(statistic=-1.637098448290542, pvalue=0.10202110497954867)

When n1 != n2, the equal variance t-statistic is no longer equal to the unequal variance t-statistic:

>>> rvs4 = stats.norm.rvs(loc=5, scale=20, size=100, random_state=rng)
>>> stats.ttest_ind(rvs1, rvs4)
Ttest_indResult(statistic=-1.9481646859513422, pvalue=0.05186270935842703)
>>> stats.ttest_ind(rvs1, rvs4, equal_var=False)
Ttest_indResult(statistic=-1.3146566100751664, pvalue=0.1913495266513811)

T-test with different means, variance, and n:

>>> rvs5 = stats.norm.rvs(loc=8, scale=20, size=100, random_state=rng)
>>> stats.ttest_ind(rvs1, rvs5)
Ttest_indResult(statistic=-2.8415950600298774, pvalue=0.0046418707568707885)
>>> stats.ttest_ind(rvs1, rvs5, equal_var=False)
Ttest_indResult(statistic=-1.8686598649188084, pvalue=0.06434714193919686)

When performing a permutation test, more permutations typically yields more accurate results. Use a np.random.Generator to ensure reproducibility:

>>> stats.ttest_ind(rvs1, rvs5, permutations=10000,
...                 random_state=rng)
Ttest_indResult(statistic=-2.8415950600298774, pvalue=0.0052)

Take these two samples, one of which has an extreme tail.

>>> a = (56, 128.6, 12, 123.8, 64.34, 78, 763.3)
>>> b = (1.1, 2.9, 4.2)

Use the trim keyword to perform a trimmed (Yuen) t-test. For example, using 20% trimming, trim=.2, the test will reduce the impact of one (np.floor(trim*len(a))) element from each tail of sample a. It will have no effect on sample b because np.floor(trim*len(b)) is 0.

>>> stats.ttest_ind(a, b, trim=.2)