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## failing to reject the null hypothesis when it is false.

2. If p is greater than alpha, the fail to reject the null hypothesis.

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# failing to reject the null hypothesis when it is true.

The null and the alternative are:

Rejection Rule: Reject the null hypothesis if F0.095 or F> F0.05 where F, the value of the test statistic is equal to s12/s22, with 10 degrees of freedom for both the numerator and the denominator.

When you about a , you can use your test statistic to decide whether to reject the null hypothesis, H0. You make this decision by coming up with a number, called a -value.

## The failure to reject does not imply the null hypothesis is true.

For example, suppose a pizza place claims their delivery times are 30 minutes or less on average but you think it’s more than that. You conduct a hypothesis test because you believe the null hypothesis, Ho, that the mean delivery time is 30 minutes max, is incorrect. Your alternative hypothesis (Ha) is that the mean time is greater than 30 minutes. You randomly sample some delivery times and run the data through the hypothesis test, and your -value turns out to be 0.001, which is much less than 0.05. In real terms, there is a probability of 0.001 that you will mistakenly reject the pizza place’s claim that their delivery time is less than or equal to 30 minutes. Since typically we are willing to reject the null hypothesis when this probability is less than 0.05, you conclude that the pizza place is wrong; their delivery times are in fact more than 30 minutes on average, and you want to know what they’re gonna do about it! (Of course, you could be wrong by having sampled an unusually high number of late pizza deliveries just by chance.)

The reported -value is 0.070. If we use an of 0.05, then the -value is greater than , so we fail to reject the null hypothesis. That is, we did not have sufficient evidence to say that there is an association between and .

## How to Determine a p-Value When Testing a Null Hypothesis

True. Just by chance it is possible to get a samplethat produces a small p-value,even though the null hypothesis is true. This is called a Type Ierror. A Type II error is when the null hypothesis is notrejectedwhen it is in fact false.

Most of the tests in this book rely on using a statistic called the -value to evaluate if we should reject, or fail to reject, the null hypothesis.

• ### fail to reject the null hypothesis

0.477395Conclusion: Since p-value is greater than the level of significance (0.05), fails to reject the null.

• ### Do you reject or fail to reject the null hypothesis?

Failure to reject the null hypothesis is ..

• ### If we fail to reject the null hypothesis, _____

Decision reject or fail to reject the null hypothesis

## will you reject or fail to reject the null hypothesis

Read your first. If the p-value is small (less than your ), you can accept the null hypothesis. Only then should you consider the f-value. If you fail to reject the null, discard the f-value result.

## fail to reject the null hypothesis - Spanish ..

Why?
The F value should always be used along with the p value in deciding whether your results are significant enough to reject the null hypothesis. If you get a large f value (one that is bigger than the F critical value found in a table), it means something is , while a small p value means all your results are significant. The F statistic just compares the joint effect of all the together. To put it simply, reject the null hypothesis only if your alpha level is larger than your p value.

## then you fail to reject the null hypothesis ..

If you want to know whether your regression F-value is significant, you’ll need to find the in the . For example, let’s say you had 3 regression (df1) and 120 residual degrees of freedom (df2). An F statistic of at least 3.95 is needed to reject the null hypothesis at an alpha level of 0.1. At this level, you stand a 1% chance of being wrong (Archdeacon, 1994, p.168). For more details on how to do this, see: . F Values will range from 0 to an arbitrarily large number.

## Either reject or fail to reject null hypothesis

• A occurs when the null hypothesis is really false, but based on our decision rule we fail to reject the null hypothesis. In this case, our result is a ; we have failed to find an effect that really does exist. The probability of making this kind of error is called .

## The other is "Fail to Reject the Null Hypothesis"

Where this could get confusing is where one of these values seems to indicate that you should reject the null hypothesis and one of the values indicates you should not. For example, let’s say your One Way ANOVA has a p value of 0.68 and an of 0.05. As the p value is large, you should not However, your f value is 4.0 with an f critical value of 3.2. Should you now reject the null hypothesis? The answer is NO.

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