Q&A 6 How do you interpret a p-value in hypothesis testing?

6.1 Explanation

The p-value represents the probability of observing the data (or something more extreme) if the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis.

6.2 Python Code

# P-value is typically output by test functions, e.g., t-test, ANOVA
# Interpret p < 0.05 as statistically significant
print("If p < 0.05, we reject the null hypothesis.")

If p < 0.05, we reject the null hypothesis.

6.3 R Code

# P-values are returned by tests such as t.test or aov
# Print interpretation guide
cat("If p < 0.05, we reject the null hypothesis.\n")

If p < 0.05, we reject the null hypothesis.