What is a good p-value in regression?

The decision to reject or accept the null hypothesis based on the P-value threshold of 0.05 involves a balance between the risks of false positives (Type I errors) and false negatives (Type II errors). A lower threshold would reduce the risk of false positives but increase the risk of missing genuine effects.

The statistical threshold of significance, commonly denoted as the P-value, is an essential concept in data analysis. A widely accepted threshold for statistical significance is 0.05, which signifies a level of certainty in the results.

The interpretation of a P-value of 0.05 implies that there is a 5% chance that the observed effect or difference is merely due to chance or random variation, rather than a genuine underlying phenomenon. This threshold is used as a benchmark to determine whether the results of a study are statistically significant.

It's crucial to understand that a P-value of 0.05 does not necessarily mean that the null hypothesis (the assumption that there is no difference or effect) is false with 95% certainty. Rather, it suggests that there is sufficient evidence to reject the null hypothesis at the chosen level of significance.

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