- Can sample evidence prove a null hypothesis is true?
- How do you fail to reject the null hypothesis?
- When we reject the null hypothesis which of the following is true?
- How do you test the null hypothesis?
- What is the relationship between type I and type II errors?
- How do you interpret a Type 1 error?
- Which error is more dangerous?
- What are the 3 types of error?

## Can sample evidence prove a null hypothesis is true?

Sample evidence can prove that a null hypothesis is true. The correct answer is False because although sample data is used to test the null hypothesis, it cannot be stated with 100% certainty that the null hypothesis is true.

## How do you fail to reject the null hypothesis?

After you perform a hypothesis test, there are only two possible outcomes.

- When your p-value is less than or equal to your significance level, you reject the null hypothesis. The data favors the alternative hypothesis.
- When your p-value is greater than your significance level, you fail to reject the null hypothesis.

## When we reject the null hypothesis which of the following is true?

When doing hypothesis testing, two types of mistakes may be made and we call them Type I error and Type II error. If we reject the null hypothesis when it is true, then we made a type I error. If the null hypothesis is false and we failed to reject it, we made another error called a Type II error.

## How do you test the null hypothesis?

The steps are as follows:

- Assume for the moment that the null hypothesis is true.
- Determine how likely the sample relationship would be if the null hypothesis were true.
- If the sample relationship would be extremely unlikely, then reject the null hypothesis in favour of the alternative hypothesis.

## What is the relationship between type I and type II errors?

A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.

## How do you interpret a Type 1 error?

A type I error occurs when the null hypothesis is true, but is rejected. Let me say this again, a type I error occurs when the null hypothesis is actually true, but was rejected as false by the testing. A type I error, or false positive, is asserting something as true when it is actually false.

## Which error is more dangerous?

The short answer to this question is that it really depends on the situation. In some cases, a Type I error is preferable to a Type II error, but in other applications, a Type I error is more dangerous to make than a Type II error.

## What are the 3 types of error?

Basically there are three types of errors in physics, random errors, blunders, and systematic errors.