A P-Value represents the likelihood of our data occurring randomly, and it’s crucial in deciding whether to accept or reject the null hypothesis. The Breusch-Pagan test, on the other hand, is used to detect heteroskedasticity. This test is running a regression where we predict the squared residuals from the initial regression model using predictor variables, and then evaluating the significance of these coefficients. If these coefficients significantly deviate from zero, it suggests the presence of heteroskedasticity. Based on the p-value, if we choose to opt the null hypothesis (H0), it implies that the data does not exhibit heteroskedasticity. Conversely, if we opt for the alternative hypothesis, it suggests the presence of heteroskedasticity in the data. If the p-value of the test falls below a specific significance threshold (e.g., α = .05), we reject the null hypothesis and infer that the regression model exhibits heteroscedasticity.