Interview question: What do you understand by statistical power of sensitivity and how do you calculate it?

Statistics is one of the skills that a data scientist must become proficient at. It is for that reason that a data scientist must be knowledgeable of statistical methods and procedures, as he may be called upon to discuss them during an interview.

A statistical hypothesis test calculates some quantity under a given assumption (null hypothesis) and the result of the test allows interpretation of whether the assumption is valid or violated. A violation of the test’s assumption is called the alternative hypothesis (HA).The p-value and critical values are the most common results of a statistical test, which can be interpreted in different ways.

The p-value is compared to the significance level, alpha.Typical significance levels can be 10%, 5% or 1%. For example:-

1. If the p-value is less than or equal to alpha, reject the null hypothesis (significant result).
2. If the p-value is greater than alpha, fail to reject the null hypothesis (not significant result).

When performing statistical tests, there are two types of errors:-

1. A type I error is when a true null hypothesis is rejected, also known as a false positive.