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(alpha and p values for data analytics)

The alpha and p values are used in statistics to help us understand if our results are reliable. The alpha value is like a cutoff point – it's the level of confidence that we want to have in our results. For example, if we set the alpha value to 0.05, it means that we are confident that our results are correct 95% of the time.

The p value is the probability that we would get our results just by chance, even if the thing we're testing (called the null hypothesis) isn't true. So if we're testing whether boys are taller than girls on average, and we get a p value of 0.01, that means there's only a 1% chance that we would see a difference in height just by chance, even if boys and girls are actually the same height on average.

If the p value is less than the alpha value, we can say that our results are reliable and that the null hypothesis is probably not true. So in the example above, if the alpha value is 0.05 and the p value is 0.01, we can say that boys are probably taller than girls on average because it's unlikely that we would see a difference in height just by chance.

Alpha and p values help us decide if our results are reliable and if we can trust them to tell us something about the world. The alpha value is the level of confidence that we want to have in our results, and the p value is the probability that we would see our results just by chance. If the p value is less than the alpha value, we can say that our results are reliable and that the null hypothesis is probably not true.