Machine Learning p-value for beginners
The p-value is a critical concept in statistical hypothesis testing, particularly in deciding whether to retain or reject a null hypothesis. Let’s explore this concept with a detailed example.
Understanding P-Value:
The p-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming that the null hypothesis is true. It is a measure of the strength of evidence against the null hypothesis.
Null Hypothesis (H0):
The null hypothesis generally posits that there is no effect or no difference. In the context of a regression model, it might state that a particular predictor’s coefficient is zero (i.e., the predictor has no impact on the outcome variable).
Alternative Hypothesis (H1):
The alternative hypothesis contradicts the null hypothesis. It suggests that the predictor does have an effect (i.e., the coefficient is not zero).
Example Scenario: Impact of Study Hours on Exam Scores
Suppose you want to understand whether the number of hours a student studies affects their exam score.
Data:
- Hours Studied: [1, 2, 3, 4, 5]
