P-value in Machine Learning – Python

Introduction-

In statistics and machine learning, the p-value (also known as- probability value) is a measure that helps determine the significance of a hypothesis test result. It indicates the probability of obtaining an observed result when the null hypothesis is true

How it Works- 

1. Null Hypothesis (H0): It dictates about the assumption that there is no difference between groups in a study.   
2. .Alternative Hypothesis (H1): It represents the assertion that there is a difference between groups.
3. The p-value is then compared to a significance level (known as- α(alpha)) which is a predetermined threshold value.

Cases-

• If the p-value is less than or equal to the significance level (p ≤ α), then the null hypothesis is rejected. This suggests that the observed result is statistically significant, and there is enough evidence to support the alternative hypothesis.
• If the p-value is greater than the significance level (p > α), then the null hypothesis is not rejected. This suggests that the observed result is not statistically significant, and there is not enough evidence to support the alternative hypothesis.

P-values are commonly used in statistical tests such as t-tests, ANOVA, and linear regression to assess the significance of model coefficients, feature importance, or differences between groups.

Sample Code-

Here is a simple python code depicting the use of p-value in T-test for two groups.

from scipy import stats

# Example data
group1 = [11, 12, 13, 14, 15]
group2 = [16, 17, 18, 19, 20]

# Perform t-test for independent samples
t_statistic, p_value = stats.ttest_ind(group1, group2)

# Print the results
print("t-statistic:", t_statistic)
print("p-value:", p_value)

Sample Output-