When we falsely reject the null hypothesis but in fact, we shouldn’t, what type of error (Type I or II) did we make? When we find no differences from the null hypothesis testing but in fact, there are differences in population, what type of error (Type I or II) did we make?

Hypothesis show

Q1.
Here is a research question: “Does the amount of time spent on Instagram per day negatively influence a student’s GPAs?”
Based on the research questions (RQ), formulate a research hypothesis (H1) and its null hypothesis (H0)

H0:

The statistical analysis we ran to test the hypothesis show p = .04. What does the p value mean here?

Can we reject the null hypothesis of question based on this p value?

What does the result mean in population?
It means:
There is 4% of the probability that _.
There is 96% of the probability that _.

Q2. When we falsely reject the null hypothesis but in fact, we shouldn’t, what type of error (Type I or II) did we make?

Q3. When we find no differences from the null hypothesis testing but in fact, there are differences in population, what type of error (Type I or II) did we make?