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---
jupytext:
text_representation:
extension: .md
format_name: myst
format_version: 0.13
jupytext_version: 1.16.1
kernelspec:
display_name: Python 3 (ipykernel)
language: python
name: python3
---
+++ {"tags": ["jupyterlite_sphinx_strip"]}
```{eval-rst}
.. notebooklite:: hypothesis_skewtest.md
:new_tab: True
```
(hypothesis_skewtest)=
+++
# Skewness test
This function tests the null hypothesis that the skewness of the population that
the sample was drawn from is the same as that of a corresponding normal
distribution.
Suppose we wish to infer from measurements whether the weights of adult human
males in a medical study are not normally distributed [^1]. The weights (lbs)
are recorded in the array `x` below.
```{code-cell}
import numpy as np
x = np.array([148, 154, 158, 160, 161, 162, 166, 170, 182, 195, 236])
```
The skewness test {func}`scipy.stats.skewtest` from [^2] begins by computing a
statistic based on the sample skewness.
```{code-cell}
from scipy import stats
res = stats.skewtest(x)
res.statistic
```
Because normal distributions have zero skewness, the magnitude of this statistic
tends to be low for samples drawn from a normal distribution.
The test is performed by comparing the observed value of the statistic against
the null distribution: the distribution of statistic values derived under the
null hypothesis that the weights were drawn from a normal distribution.
For this test, the null distribution of the statistic for very large samples is
the standard normal distribution.
```{code-cell}
import matplotlib.pyplot as plt
dist = stats.norm()
st_val = np.linspace(-5, 5, 100)
pdf = dist.pdf(st_val)
fig, ax = plt.subplots(figsize=(8, 5))
def st_plot(ax): # we'll reuse this
ax.plot(st_val, pdf)
ax.set_title("Skew Test Null Distribution")
ax.set_xlabel("statistic")
ax.set_ylabel("probability density")
st_plot(ax)
plt.show()
```
The comparison is quantified by the p-value: the proportion of values in the
null distribution as extreme or more extreme than the observed value of the
statistic. In a two-sided test, elements of the null distribution greater than
the observed statistic and elements of the null distribution less than the
negative of the observed statistic are both considered "more extreme".
```{code-cell}
fig, ax = plt.subplots(figsize=(8, 5))
st_plot(ax)
pvalue = dist.cdf(-res.statistic) + dist.sf(res.statistic)
annotation = (f'p-value={pvalue:.3f}\n(shaded area)')
props = dict(facecolor='black', width=1, headwidth=5, headlength=8)
_ = ax.annotate(annotation, (3, 0.005), (3.25, 0.02), arrowprops=props)
i = st_val >= res.statistic
ax.fill_between(st_val[i], y1=0, y2=pdf[i], color='C0')
i = st_val <= -res.statistic
ax.fill_between(st_val[i], y1=0, y2=pdf[i], color='C0')
ax.set_xlim(-5, 5)
ax.set_ylim(0, 0.1)
plt.show()
```
```{code-cell}
res.pvalue
```
If the p-value is "small" - that is, if there is a low probability of sampling
data from a normally distributed population that produces such an extreme value
of the statistic - this may be taken as evidence against the null hypothesis in
favor of the alternative: the weights were not drawn from a normal distribution.
Note that:
- The inverse is not true; that is, the test is not used to provide
evidence for the null hypothesis.
- The threshold for values that will be considered "small" is a choice that
should be made before the data is analyzed [^3] with consideration of the
risks of both false positives (incorrectly rejecting the null hypothesis)
and false negatives (failure to reject a false null hypothesis).
Note that the standard normal distribution provides an asymptotic approximation
of the null distribution; it is only accurate for samples with many
observations. For small samples like ours, {class}`scipy.stats.monte_carlo_test`
may provide a more accurate, albeit stochastic, approximation of the exact
p-value.
```{code-cell}
def statistic(x, axis):
# get just the skewtest statistic; ignore the p-value
return stats.skewtest(x, axis=axis).statistic
res = stats.monte_carlo_test(x, stats.norm.rvs, statistic)
fig, ax = plt.subplots(figsize=(8, 5))
st_plot(ax)
ax.hist(res.null_distribution, np.linspace(-5, 5, 50),
density=True)
ax.legend(['asymptotic approximation\n(many observations)',
'Monte Carlo approximation\n(11 observations)'])
plt.show()
```
```{code-cell}
res.pvalue
```
In this case, the asymptotic approximation and Monte Carlo approximation agree
fairly closely, even for our small sample.
## References
[^1]: Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for
normality (complete samples). Biometrika, 52(3/4), 591-611.
[^2]: R. B. D'Agostino, A. J. Belanger and R. B. D'Agostino Jr. (1990). A
suggestion for using powerful and informative tests of normality. American
Statistician 44, pp. 316-321.
[^3]: Phipson, B. and Smyth, G. K. (2010). Permutation P-values Should Never Be
Zero: Calculating Exact P-values When Permutations Are Randomly Drawn.
Statistical Applications in Genetics and Molecular Biology 9.1.
|
