## When should a null hypothesis be rejected?

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.

## Why would you fail to reject the null hypothesis?

The goal of **hypothesis** testing is to see if there is enough evidence against the **null hypothesis**. In other words, to see if there is enough evidence to **reject the null hypothesis**. If there is not enough evidence, then **we fail to reject the null hypothesis**.

## Do you reject or fail to reject H0 at the 0.05 level of significance?

**We reject** the **null hypothesis** when the p-value is less than α. But 0.07 > **0.05** so **we fail to reject H0**. For example if the p-value = 0.08, then **we would fail to reject H0** at the **significance level** of α=**0.05** since 0.08 > **0.05**, but **we would reject H0** at the **significance level** of α = 0.10 since 0.08 < 0.10.

## How do you determine if the null hypothesis is rejected?

Typically, **if** there was a 5% or less chance (5 times in 100 or less) that the difference in the mean exam performance between the two teaching methods (or whatever statistic you are using) is as different as observed given the **null hypothesis** is true, you would **reject** the **null hypothesis** and accept the alternative

## How do you accept or reject the null hypothesis?

In **Hypothesis** testing, if the significance value of the test is greater than the predetermined significance level, then we **accept** the **null hypothesis**. If the significance value is less than the predetermined value, then we should **reject** the **null hypothesis**.

## How do you write a Failed to reject the null hypothesis?

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

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

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

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 _{}.

## 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.

## When you reject the null hypothesis when the null hypothesis is true this type of error is called?

Two **types of error** are distinguished: **Type I error** and **type** II **error**. The first **kind of error** is the **rejection** of a **true null hypothesis** as the result of a test procedure. This **kind of error is called** a **type I error** (false positive) and is sometimes **called** an **error** of the first **kind**.

## 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**.

## What does p value 0.05 mean?

**P** > **0.05 is the** probability that the null hypothesis is true. 1 minus the **P value is the** probability that the alternative 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 do you choose the null hypothesis and alternative hypothesis?

The rule for the proper formulation of a **hypothesis** test is that the **alternative** or research **hypothesis** is the statement that, if true, is strongly supported by the evidence furnished by the data. The **null hypothesis** is generally the complement of the **alternative hypothesis**.

## Can you accept a null hypothesis?

**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.

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

The p-value is the probability of observing such a sample mean **when** the null **hypothesis** is true. **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.