# scipy.stats.ttest_1samp

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

  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
    a array_like

      Sample observation.

    popmean float or array_like

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

    axis int 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
    statistic float or array

      t-statistic.

    pvalue float 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]])

scipy.stats.multiscale_graphcorr scipy.stats.ttest_ind