scipy.stats.ttest_1samp#

scipy.stats.ttest_1samp(a, popmean, axis=0, nan_policy='propagate', alternative='two-sided')[source]#

Calculate the T-test for the mean of ONE group of scores.

This is a test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given population mean, popmean.

Parameters
aarray_like

Sample observation.

popmeanfloat or array_like

Expected value in null hypothesis. If array_like, then it must have the same shape as a excluding the axis dimension.

axisint or None, optional

Axis along which to compute test; default is 0. If None, compute over the whole array a.

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

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

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

  • ‘two-sided’: the mean of the underlying distribution of the sample is different than the given population mean (popmean)

  • ‘less’: the mean of the underlying distribution of the sample is less than the given population mean (popmean)

  • ‘greater’: the mean of the underlying distribution of the sample is greater than the given population mean (popmean)

New in version 1.6.0.

Returns
statisticfloat or array

t-statistic.

pvaluefloat or array

Two-sided p-value.

Examples

>>> from scipy import stats
>>> rng = np.random.default_rng()
>>> rvs = stats.norm.rvs(loc=5, scale=10, size=(50, 2), random_state=rng)

Test if mean of random sample is equal to true mean, and different mean. We reject the null hypothesis in the second case and don’t reject it in the first case.

>>> stats.ttest_1samp(rvs, 5.0)
Ttest_1sampResult(statistic=array([-2.09794637, -1.75977004]), pvalue=array([0.04108952, 0.08468867]))
>>> stats.ttest_1samp(rvs, 0.0)
Ttest_1sampResult(statistic=array([1.64495065, 1.62095307]), pvalue=array([0.10638103, 0.11144602]))

Examples using axis and non-scalar dimension for population mean.

>>> result = stats.ttest_1samp(rvs, [5.0, 0.0])
>>> result.statistic
array([-2.09794637,  1.62095307])
>>> result.pvalue
array([0.04108952, 0.11144602])

>>> result = stats.ttest_1samp(rvs.T, [5.0, 0.0], axis=1)
>>> result.statistic
array([-2.09794637,  1.62095307])
>>> result.pvalue
array([0.04108952, 0.11144602])

>>> result = stats.ttest_1samp(rvs, [[5.0], [0.0]])
>>> result.statistic
array([[-2.09794637, -1.75977004],
       [ 1.64495065,  1.62095307]])
>>> result.pvalue
array([[0.04108952, 0.08468867],
       [0.10638103, 0.11144602]])

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