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# scipy.stats.ttest_1samp

`scipy.stats.``ttest_1samp`(a, popmean, axis=0)

  Calculates 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 than it must have the same shape as a excluding the axis dimension axis : int or None, optional Axis along which to compute test. If None, compute over the whole array a.
  Returns: | statistic : float or array t-statistic pvalue : float or array two-tailed p-value

  Examples

      >>> from scipy import stats

      >>> np.random.seed(7654567)  # fix seed to get the same result
      >>> rvs = stats.norm.rvs(loc=5, scale=10, size=(50,2))

  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)
      (array([-0.68014479, -0.04323899]), array([ 0.49961383,  0.96568674]))
      >>> stats.ttest_1samp(rvs,0.0)
      (array([ 2.77025808,  4.11038784]), array([ 0.00789095,  0.00014999]))

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

      >>> stats.ttest_1samp(rvs,[5.0,0.0])
      (array([-0.68014479,  4.11038784]), array([  4.99613833e-01,   1.49986458e-04]))
      >>> stats.ttest_1samp(rvs.T,[5.0,0.0],axis=1)
      (array([-0.68014479,  4.11038784]), array([  4.99613833e-01,   1.49986458e-04]))
      >>> stats.ttest_1samp(rvs,[[5.0],[0.0]])
      (array([[-0.68014479, -0.04323899],
             [ 2.77025808,  4.11038784]]), array([[  4.99613833e-01,   9.65686743e-01],
             [  7.89094663e-03,   1.49986458e-04]]))