Q: When should you accept the null?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

You should reject the null hypothesis.

No, you are never certain.

Be able to reject the null hypothesis and accept the research hypothesis

This is the set of natural numbers.

The null and alternative hypotheses are not calculated. They should be determined before any data analyses are carried out.

Related questions

You should reject the null hypothesis.

No, you are never certain.

yes

Be able to reject the null hypothesis and accept the research hypothesis

Be able to reject the null hypothesis and accept the research hypothesis

Be able to reject the null hypothesis and accept the research hypothesis

The z-score is a statistical test of significance to help you determine if you should accept or reject the null-hypothesis; whereas the p-value gives you the probability that you were wrong to reject the null-hypothesis. (The null-hypothesis proposes that NO statistical significance exists in a set of observations).

Some people say you can either accept the null hypothesis or reject it. However, there are statisticians that insist you can either reject it or fail to reject it, but you can't accept it because then you're saying it's true. If you fail to reject it, you're only claiming that the data wasn't strong enough to convince you to choose the alternative hypothesis over the null hypothesis.

We have two types of hypothesis i.e., Null Hypothesis and Alternative Hypothesis. we take null hypothesis as the same statement given in the problem. Alternative hypothesis is the statement that is complementary to null hypothesis. When our calculated value is less than the tabulated value, we accept null hypothesis otherwise we reject null hypothesis.

You accept an alternative hypothesis when the p-value is greater than the sample a for a confidence level of 95%. The null hypothesis cannot be accepted.

Using the data in the Excel file Consumer Transporta

This is used in statistic to know whether to accept or reject a null hypothesis or alternative hypothesis