Thread by @rohanpaul_ai

But what p-value means in #MachineLearning - A thread

It tells you how likely it is that your data could have occurred under the null hypothesis

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What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

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A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

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P values address only one question:
How likely are your data, assuming a true null hypothesis ?

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p-value does this by calculating the likelihood of your test statistic. The p-value gets smaller as the test statistic gets further away from the range of test statistics predicted by the null hypothesis.

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The p-value is a proportion: if your p-value is 0.05, that means that 5% of the time you would see a test statistic at least as extreme as the one you found if the null hypothesis was true.

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What is significance level

The significance level, or α, is the probability of rejecting the null hypothesis when it is true. e.g. a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference

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Compare your p-value to your significance level. If the p-value is less than your significance level, you can reject the null hypothesis and conclude that the effect is statistically significant.

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If P value is more than significance level we accept the null hypothesis or fail to reject it,

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So, P-values are often interpreted as your risk of rejecting the null hypothesis of your test when the null hypothesis is actually true.

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