scipy.stats.ttest_1samp¶
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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 two-sided 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’
‘less’: one-sided
‘greater’: one-sided
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]])