Contents

- 1 When should we reject the null hypothesis?
- 2 How do you know if you reject or fail to reject?
- 3 When should you reject a test?
- 4 Is it easier to reject the null hypothesis?
- 5 Do you reject null hypothesis p value?
- 6 What does reject the null hypothesis mean?
- 7 What happens when you fail to reject the null hypothesis?
- 8 What does p value 0.05 mean?
- 9 How should you interpret a decision that fails to reject the null hypothesis?
- 10 How do you reject the null hypothesis for an F test?
- 11 What is p-value formula?
- 12 Why do we reject the null hypothesis when the p-value is small?
- 13 When we reject the null hypothesis when it is actually false we have committed?
- 14 What does a significance level of 0.01 mean?
- 15 How do you fix a Type 1 error?

## When should we reject the null hypothesis?

Set the significance level,, the probability of making a Type **I** error to be small — 0.01, 0.05, or 0.10. Compare the P-value to. If the P-value is less than (or equal to), **reject the null hypothesis** in favor of the alternative **hypothesis**. If the P-value is greater than, **do** not **reject the null hypothesis**.

## How do you know if you reject or fail to reject?

Suppose **that you** do a hypothesis test. Remember **that** the decision to **reject** the null hypothesis (H _{}) or **fail to reject** it **can** be based on the p-value and **your** chosen significance level (also called α). **If** the p-value is less than or equal to α, **you reject** H _{}; **if** it is greater than α, **you fail to reject** H _{}.

## When should you reject a test?

If the absolute value of the t-value is greater than the critical value, you **reject** the null hypothesis. If the absolute value of the t-value is less than the critical value, you fail to **reject** the null hypothesis.

## Is it easier to reject the null hypothesis?

Higher values of α make it **easier to reject the null hypothesis**, so choosing higher values for α can reduce the probability of a Type II error.

## Do you reject null hypothesis p value?

If the **p**–**value** is less than 0.05, **we reject** the **null hypothesis** that there’s no difference between the means and conclude that a significant difference does exist. If the **p**–**value** is larger than 0.05, **we** cannot conclude that a significant difference exists.

## 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**. When this happens, the result is said to be statistically significant.

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

When **we fail to reject the null hypothesis** when the **null hypothesis** is false. The “reality”, or truth, about the **null hypothesis** is unknown and therefore **we** do not know if **we** have made the correct decision or if **we** committed an error. **We** can, however, define the likelihood of these events.

## What does p value 0.05 mean?

**P** > **0.05 is the** probability that the null hypothesis is true. A statistically significant test result (**P** ≤ **0.05**) **means** that the test hypothesis is false or should be rejected. A P **value** greater than **0.05 means** that no effect was observed.

## How should you interpret a decision that fails to reject the null hypothesis?

There is enough evidence to **reject** the claim. e) **How should you interpret a decision that fails to reject the null hypothesis**? There is not enough evidence to **reject** the claim.

## How do you reject the null hypothesis for an F test?

When you have found the **F** value, you can compare it with an **f** critical value in the table. If your observed value of **F** is larger than the value in the **F** table, then you can **reject** the **null hypothesis** with 95 percent confidence that the variance between your two populations isn’t due to random chance.

## What is p-value formula?

The **p**–**value** is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test). The **p**–**value** for: an upper-tailed test is specified by: **p**–**value** = **P**(TS ts | H _{} is true) = 1 – cdf(ts)

## Why do we reject the null hypothesis when the p-value is small?

A **p**–**value** less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the **null hypothesis**, as there is less than a 5% probability the **null** is correct (and the results **are** random). Therefore, **we reject the null hypothesis**, and accept the alternative **hypothesis**.

## When we reject the null hypothesis when it is actually false we have committed?

The other two are errors. If **we reject** a true **null hypothesis**, **we have committed** a type **I** error. If **we** accept a **false null hypothesis**, **we have** made a type II error. Each of these four possibilities has some probability of occurring, and those probabilities are contingent on whether the **null hypothesis** is true or **false**.

## What does a significance level of 0.01 mean?

The lower the **significance level**, the more the data must diverge from the null hypothesis to be **significant**. Therefore, the **0.01 level** is more conservative than the 0.05 **level**. The Greek letter alpha (α) is sometimes used to indicate the **significance level**.

## How do you fix a Type 1 error?

∎ **Type I Error**.

If the null hypothesis is true, then the probability of making a **Type I error** is equal to the significance level of the test. To decrease the probability of a **Type I error**, decrease the significance level. Changing the sample size has no effect on the probability of a **Type I error**.