Summary and Practical Guide to Hypothesis Testing › scipy.stats Quick Reference
Michael BrenndoerferJanuary 10, 20261 min read
Machine Learning from ScratchAll chapters Part 2 of 9Hypothesis Testing
Summary and Practical Guide to Hypothesis Testing › scipy.stats Quick ReferenceOne section · Sections in this chapter9 / 12 ↓
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Testing Functions
In[2]:
Codefrom scipy import stats
# One-sample tests
stats.ttest_1samp(sample, popmean) # One-sample t-test
stats.shapiro(sample) # Normality test
# Two-sample tests
stats.ttest_ind(a, b, equal_var=True) # Pooled t-test
stats.ttest_ind(a, b, equal_var=False) # Welch's t-test
stats.ttest_rel(a, b) # Paired t-test
stats.levene(a, b) # Equal variance test
# Multiple groups
stats.f_oneway(g1, g2, g3, ...) # One-way ANOVA
stats.tukey_hsd(g1, g2, g3, ...) # Tukey post-hoc
stats.dunnett(t1, t2, control=ctrl) # Dunnett post-hoc
# Multiple comparison corrections
stats.false_discovery_control(pvals, method="bh") # BH correction
stats.false_discovery_control(pvals, method="by") # BY correctionDistribution Functions
In[4]:
Code# For manual calculations
stats.norm.ppf(0.975) # Z critical value (two-tailed, α=0.05)
stats.t.ppf(0.975, df) # t critical value
stats.f.ppf(0.95, df1, df2) # F critical value
# P-values from test statistics
stats.norm.sf(z) * 2 # Two-tailed p from z
stats.t.sf(t, df) * 2 # Two-tailed p from t
stats.f.sf(f, df1, df2) # Upper-tail p from F