# What happens if null hypothesis is accepted?

If we accept the null hypothesis, we are stating that our data are consistent with the null hypothesis (recognizing that other hypotheses might also be consistent with the data). If we reject the null hypothesis, we are stating that our data are so unexpected that they are inconsistent with the null hypothesis.

## What happens if you fail to reject the null hypothesis?

If we fail to reject the null hypothesis, it does not mean that the null hypothesis is true. That's because a hypothesis test does not determine which hypothesis is true, or even which one is very much more likely.

## Why we never accept the null hypothesis?

Why can't we say we “accept the null”? The reason is that we are assuming the null hypothesis is true and trying to see if there is evidence against it. Therefore, the conclusion should be in terms of rejecting the null.

## Is the null hypothesis ever accepted?

Does one reject the null hypothesis? In common usage, when one does not reject something, one is accepting it. This seems logical since accept and reject are antonyms (opposites). However, in null hypothesis significance testing, one can never accept the null hypothesis.

## What does it mean if a hypothesis is accepted or rejected?

When the null hypothesis is rejected it means the sample has done some statistical work, but when the null hypothesis is accepted it means the sample is almost silent. The behavior of the sample should not be used in favor of the null hypothesis.

## What does it mean when hypothesis is accepted?

If the tabulated value in hypothesis testing is more than the calculated value, than the null hypothesis is accepted. Otherwise it is rejected. The last step of this approach of hypothesis testing is to make a substantive interpretation. The second approach of hypothesis testing is the probability value approach.

## Is the rejection of a null hypothesis that should be accepted?

Rejecting or failing to reject the null hypothesis

If our statistical analysis shows that the significance level is below the cut-off value we have set (e.g., either 0.05 or 0.01), we reject the null hypothesis and accept the alternative hypothesis.

## What null hypothesis tells us?

The null hypothesis is a typical statistical theory which suggests that no statistical relationship and significance exists in a set of given single observed variable, between two sets of observed data and measured phenomena.

## Why is rejecting the null hypothesis a strong conclusion?

The rejection of the null hypothesis H0 is a strong statement that H0 does not appear to be consistent with the observed data. The result that H0 is not rejected is a weak statement that should be interpreted to mean that H0 is consistent with the data.

## What happens if we reject the null hypothesis when it is actually true?

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 know when to reject the null hypothesis?

In null hypothesis testing, this criterion is called α (alpha) and is almost always set to . 05. If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. When this happens, the result is said to be statistically significant .

## What does rejecting the null hypothesis mean?

After a performing a test, scientists can: Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or. Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena)

## Does rejecting the null mean it is false?

If we reject the null hypothesis, we are stating that our data are so unexpected that they are inconsistent with the null hypothesis. Our decision will change our behavior. 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.

## Is failing to reject the null the same as accepting the null?

Showing that the null hypothesis is true is not the same thing as failing to reject it. There is a relatively low probability (by construction) of rejecting the null hypothesis when it is in fact true (Type I error).

## Does rejecting the null hypothesis prove that the alternative hypothesis is true?

No. The p-value only tells you how likely the data you have observed is to have occurred under the null hypothesis. If the p-value is below your threshold of significance (typically p < 0.05), then you can reject the null hypothesis, but this does not necessarily mean that your alternative hypothesis is true.

## Is failing to reject the null the same as accepting the null?

Showing that the null hypothesis is true is not the same thing as failing to reject it. There is a relatively low probability (by construction) of rejecting the null hypothesis when it is in fact true (Type I error).

## What type of error occurs if you fail to reject h0?

A TYPE II Error occurs when we fail to Reject Ho when, in fact, Ho is False. In this case we fail to reject a false null hypothesis.

## Does rejecting the null hypothesis prove that the alternative hypothesis is true?

No. The p-value only tells you how likely the data you have observed is to have occurred under the null hypothesis. If the p-value is below your threshold of significance (typically p < 0.05), then you can reject the null hypothesis, but this does not necessarily mean that your alternative hypothesis is true.

## What is the conclusion if we reject H0?

Option 1) Reject the null hypothesis (H0). This means that you have enough statistical evidence to support the alternative claim (H1). Option 2) Fail to reject the null hypothesis (H0). This means that you do NOT have enough evidence to support the alternative claim (H1).

## When H0 is false and accepted?

Accepting H0 when H0 is false is referred to as a Type II error, and ß = probability of a Type II error. Put another way - if α = . 05 and H0 is true, there is only a 5% chance that we will falsely reject the null hypothesis.
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