Question

In your own words, explain what sampling error is.

Why is sampling error such an issue when it comes to inferential statistics.

What is alpha? What does it represent in hypothesis testing?

Now that you know a little more about hypothesis testing, how do you feel about the fact that hypothesis testing will never give you a certain answer—that there’s always a possibility of creating a Type I or Type II error?

Answer 1

Sampling error is nothing but the error occurred in the statistical analysis which is come from the un-representativeness of the sample selected for the analysis. This error is due to sample and not due to the population under study. The sampling error is defined as the difference between the sample statistic and the actual value of population parameter. Sampling error is an issue when it comes to inferential statistics, because in inferential statistics we infer about population parameters by using the sample statistic. Alpha in the testing hypothesis represent the level of significance and it indicate at what level of significance the results are valid. In testing of hypothesis, we reject or do not reject the null hypothesis at the certain level of significance which means we would not completely reject or accept the claims or hypothesis. Although we reject some claim, there is some probability of do not rejecting the claim. So, we are never 100% confident about the results of testing of hypothesis. If we reject the null hypothesis even though it is true then type I error is occurred and when we do not reject the null hypothesis even though it is not true then type II error is occurred. This means there is a chance of being faulty results for sometimes.

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