P Value Method

The P Value Method is the most widely used approach to hypothesis testing in modern statistics and Data Science. Instead of comparing to a critical value, it directly measures how likely your observed data is, assuming the null hypothesis is true.

What a P-Value Means

The p-value is the probability of observing a result as extreme as (or more extreme than) your sample data, assuming the null hypothesis is correct. A small p-value suggests your observed data would be unlikely if the null hypothesis were really true.

Making the Decision

from scipy import stats
import numpy as np

group_a = np.array([22, 25, 21, 24, 23])
group_b = np.array([28, 30, 27, 29, 31])

t_stat, p_value = stats.ttest_ind(group_a, group_b)
print(p_value)

alpha = 0.05   # significance level

if p_value < alpha:
    print("Reject the null hypothesis - result is statistically significant")
else:
    print("Fail to reject the null hypothesis")

Common Significance Levels

0.05 (5%) is the most widely used threshold, though 0.01 and 0.10 are also used depending on how strict the field or decision requires the evidence to be.

Coming Up Next

Next, you'll learn about the types of errors that can occur when making a decision in hypothesis testing.