Question

How would you define the null hypothesis?

How would you define the alternative hypothesis?

How do you know when it is a left tailed/right tailed/or two tailed test?

What is a p value and how can you use it to test a hypothesis?

What is a p-value and how can you use it to test a hypothesis? Give your description of it in your own words that make sense to you.

What does a low p-value mean? What about a high p-value?

What are critical values and what do we mean by alpha? Why are they essential to hypothesis testing and practical vs statistical significance?

Answer 1

In inferential statistics, the null hypothesis is a general statement or default position that there is no relationship between two measured phenomena, or no association among groups.

The alternative hypothesis is the hypothesis used in hypothesis testing that is contrary to the null hypothesis. It is usually taken to be that the observations are the result of a real effect (with some amount of chance variation superposed).

Based on alternative hypothesis we decide whether it is left tailed/right tailed/or two tailed test. If alternative hypothesis have greater than sign it is right tailed, if less than sign it is left tailed and if in-equality sign it is two tailed test

The p-value is the level of marginal significance within a statistical hypothesis test representing the probability of the occurrence of a given event. The p-value is used as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected.