Contents

- 1 How do you know if there is sufficient evidence to reject the null hypothesis?
- 2 How much evidence do we need to conclude that a hypothesis is true?
- 3 Is there enough evidence to reject the claim?
- 4 How do you know if the hypothesis is accepted?
- 5 How do you know if there is sufficient evidence in stats?
- 6 What does reject the null hypothesis mean?
- 7 Is a hypothesis a prediction?
- 8 What is the probability of committing a Type I error?
- 9 Does the original claim contain the condition of equality?
- 10 Is there significant evidence at the 5 level?
- 11 What do you call the error of accepting a false hypothesis?
- 12 Is H0 the claim?
- 13 What is meant by a type 1 error?
- 14 What is p-value in hypothesis testing?

## How do you know if there is sufficient evidence to reject the null hypothesis?

Support or reject null hypothesis? If the P-value is less, reject the null hypothesis. If the P-value is more, keep the null hypothesis. 0.003 < 0.05, so we have enough evidence to reject the null hypothesis and accept the claim.

## How much evidence do we need to conclude that a hypothesis is true?

Upon analysis of the results, a hypothesis can be rejected or modified, but it can never be proven to be correct 100 percent of the time. For example, relativity has been tested many times, so it is generally accepted as true, but there could be an instance, which has not been encountered, where it is not true.

## Is there enough evidence to reject the claim?

than the significance level of α = 0.05, we reject the null hypothesis of equal means. There is sufficient evidence to warrant rejection of the claim that the three samples come from populations with means that are all equal.

## How do you know if the 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.

## How do you know if there is sufficient evidence in stats?

If the p-value is less than α, we reject the null hypothesis. If the probability is too small (less than the level of significance), then we believe we have enough statistical evidence to reject the null hypothesis and support the alternative claim.

## What does reject the null hypothesis mean?

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. This does not necessarily mean that the researcher accepts the null hypothesis as true—only that there is not currently enough evidence to conclude that it is true.

## Is a hypothesis a prediction?

defined as a proposed explanation (and for typically a puzzling observation). A hypothesis is not a prediction. Rather, a prediction is derived from a hypothesis. A causal hypothesis and a law are two different types of scientific knowledge, and a causal hypothesis cannot become a law.

## What is the probability of committing a Type I error?

The probability of making a type I error is represented by your alpha level (α), which is the p-value below which you reject the null hypothesis. A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.

## Does the original claim contain the condition of equality?

If the original claim includes equality (<=, =, or >=), it is the null hypothesis. If the original claim does not include equality (<, not equal, >) then the null hypothesis is the complement of the original claim. The null hypothesis always includes the equal sign. The decision is based on the null hypothesis.

## Is there significant evidence at the 5 level?

At the 5% significance level we have good (not strong) evidence to reject the null hypothesis since the p- value is less than 5%. At the 1% significance level we don’t have enough evidence to reject the null hypotheses since the p- value is greater than 1%.

## What do you call the error of accepting a false hypothesis?

A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one accepts a null hypothesis that is actually false. A type II error produces a false negative, also known as an error of omission.

## Is H0 the claim?

A null hypothesis, H0, is the claim tested by a statistical test. My null hypothesis is the same as your claim.

## What is meant by a type 1 error?

A type I error is a kind of fault that occurs during the hypothesis testing process when a null hypothesis is rejected, even though it is accurate and should not be rejected. In hypothesis testing, a null hypothesis is established before the onset of a test. These false positives are called type I errors.

## What is p-value in hypothesis testing?

In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.