Frequently Based Questions(FAQs) on P-Value

Q. Why we calculate the p-value?

Calculating the p-value is essential for hypothesis testing. It assesses the likelihood of observed results under the null hypothesis. A low p-value provides strong evidence against the null hypothesis, aiding in valid statistical inferences.

Q. How to compute the p-value and t value in Python?

P-value: Probability of obtaining observed results assuming the null hypothesis is true. Calculated using scipy.stats.t.sf(abs(t_score), df=degree_of_freedom).

T-value: Measures the difference between the sample mean and hypothesized mean in terms of standard error. Calculated using specific formulas for one-sample and two-sample t-tests.

Q.How is p-value related to T-score?

The p-value is associated with the t-score, a measure of how many standard deviations a sample mean is from the hypothesized mean. A larger absolute t-score corresponds to a smaller p-value.

In the realm of statistical analysis, the p-value stands as a pivotal metric, guiding researchers in drawing meaningful conclusions from their data. This article delves into the significance and computation of p-values in Python, focusing on the t-test, a fundamental statistical tool.