# What happens if we accept the null hypothesis when it is actually false?

## What happens if we accept the null hypothesis when it is actually false?

If we reject the null hypothesis, we will act as if the null hypothesis is false, even though we do not know if that is in fact false. If we accept the null hypothesis, we will act as if the null hypothesis is true, even though we have not demonstrated that it is in fact true.

### When the hypothesis is false and the test accepts it this is called?

We do not know whether it is true or false. Hence four possibilities may arise . The hypothesis is true but test rejects it ( Type I error) The hypothesis is false but test accepts it (Type II error) The hypothesis is true and test accepts it ( correct decision )

How do you verify whether a hypothesis is true or false?

Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the null hypothesis and the alternative hypothesis. However, one of the two hypotheses will always be true.

What should you do if you prove your null hypothesis?

The steps are as follows:

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

## Can null hypothesis be accepted?

Null hypothesis are never accepted. We either reject them or fail to reject them. Failing to reject a hypothesis means a confidence interval contains a value of “no difference”. However, the data may also be consistent with differences of practical importance.

### When you reject the null hypothesis is there sufficient evidence?

Option 1) Reject the null hypothesis (H0). This means that you have enough statistical evidence to support the alternative claim (H1).

What is a Type 1 error example?

In statistical hypothesis testing, a type I error is the mistaken rejection of the null hypothesis (also known as a “false positive” finding or conclusion; example: “an innocent person is convicted”), while a type II error is the mistaken acceptance of the null hypothesis (also known as a “false negative” finding or …

What is the difference between Type I and Type II error?

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.

## What is hypothesis example?

Examples of Hypothesis:

• If I replace the battery in my car, then my car will get better gas mileage.
• If I eat more vegetables, then I will lose weight faster.
• If I add fertilizer to my garden, then my plants will grow faster.
• If I brush my teeth every day, then I will not develop cavities.

### What causes a Type 1 error?

A type I error occurs during hypothesis testing when a null hypothesis is rejected, even though it is accurate and should not be rejected. The null hypothesis assumes no cause and effect relationship between the tested item and the stimuli applied during the test.

How do you know if you should reject the null hypothesis?

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

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

How do you know when to reject the null hypothesis?

## Can a hypothesis be shown to be false?

If the event is very unusual, then you might think that your assumption is actually false. If you are able to say this assumption is false, then your hypothesis must be true. This is known as a proof by contradiction. You assume the opposite of your hypothesis is true and show that it can’t be true.

### Do you need to collect data to test a hypothesis?

For a statistical test to be valid, it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

Which is the first step in hypothesis testing?

There are 5 main steps in hypothesis testing: State your research hypothesis as a null (H o) and alternate (H a) hypothesis. Collect data in a way designed to test the hypothesis. Perform an appropriate statistical test.

What kind of error is a false positive?

•Type I error, also known as a“false positive”: the error of rejecting a null hypothesis when it is actually true. In other words, this is the error of accepting an alternative hypothesis (the real hypothesis of interest) when the results can be attributed to chance.