code: def check_sample_var(sv, n, popvar):
      # two-sided chisquare test for sample variance equal to
      # hypothesized variance
      df = n - 1
      chi2 = (n - 1) * sv / popvar
      pval = stats.distributions.chi2.sf(chi2, df) * 2
      npt.assert_(
          pval > 0.01,
          "var fail, t, pval = %f, %f, v, sv=%f, %f"
          % (chi2, pval, popvar, sv),
      )
  
imports: import numpy
  import numpy.testing
  import pytest
  from pytest import raises
  from scipy.integrate import IntegrationWarning
  from scipy import stats
  from scipy.special import betainc
  from .common_tests import check_normalization, check_moment, check_mean_expect, check_var_expect, check_skew_expect, check_kurt_expect, check_entropy, check_private_entropy, check_entropy_vect_scale, check_edge_support, check_named_args, check_random_state_property, check_meth_dtype, check_ppf_dtype, check_cmplx_deriv, check_pickling, check_rvs_broadcast, check_freezing
  from scipy.stats._distr_params import distcont
label_desc: Use image node_id 17 for calling a global function with example usage: check_sample_var(sv, n, popvar) without return types