Critical Value Method

The Critical Value Method is one of the two standard approaches to hypothesis testing. It works by comparing your test statistic to a threshold value - the critical value - to decide whether to reject the null hypothesis.

How It Works

1. Choose a significance level (commonly 0.05, or 5%)
2. Determine the critical value from the relevant distribution (e.g. Normal or t-distribution) based on that significance level
3. Calculate the test statistic from your sample data
4. Compare the test statistic to the critical value

Making the Decision

If the test statistic falls beyond the critical value (in the "rejection region"), you reject the null hypothesis. Otherwise, you fail to reject it.

A Simple Example

from scipy import stats

# For a 95% confidence, two-tailed test, the critical Z-value is approximately:
critical_value = stats.norm.ppf(0.975)
print(critical_value)   # approximately 1.96

test_statistic = 2.3   # calculated from your sample

if abs(test_statistic) > critical_value:
    print("Reject the null hypothesis")
else:
    print("Fail to reject the null hypothesis")

Coming Up Next

Next, you'll learn the P Value Method - the second major approach to hypothesis testing, and the one used most often in modern practice.