When should the null hypothesis be true?

When should the null hypothesis be true?

If there is greater than a 5% chance of a result as extreme as the sample result when the null hypothesis is true, then the null hypothesis is retained. 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.

When the null hypothesis has been true but the sample information has resulted in the rejection of the null A has been made?

when we reject true null hypothesis , this will be a TYPE I error . When the null hypothesis has been true, but the sample information has resulted in the rejection of the null, a TYPE I ERROR has been made.

Can the null and alternative hypothesis both be true?

The null and alternative hypotheses are two mutually exclusive statements about a population. A hypothesis test uses sample data to determine whether to reject the null hypothesis. The alternative hypothesis is what you might believe to be true or hope to prove true.

How do you know to accept or 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.

Can sample evidence prove a null hypothesis is true?

Sample evidence can prove that a null hypothesis is true. The correct answer is False because although sample data is used to test the null​ hypothesis, it cannot be stated with​ 100% certainty that the null hypothesis is true.

What is a null hypothesis example?

A null hypothesis is a type of hypothesis used in statistics that proposes that there is no difference between certain characteristics of a population (or data-generating process). For example, a gambler may be interested in whether a game of chance is fair.

How do you write a null hypothesis in statistics?

The null statement must always contain some form of equality (=, ≤ or ≥) Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <).

When to reject or accept null hypothesis?

If the sample does not support the null hypothesis, we reject it on the probability basis and accept the alternative hypothesis. If the sample does not oppose the hypothesis, the hypothesis is accepted.

When should a null hypothesis be rejected or accepted?

If our statistical analysis shows that the significance level is below the cut-off value we have set (e.g., either 0.05 or 0.01), we reject the null hypothesis and accept the alternative hypothesis. Alternatively, if the significance level is above the cut-off value, we fail to reject the null hypothesis and cannot accept the alternative hypothesis.

Do I reject or accept the null?

You should never accept the null hypothesis. You should reject it, or fail to reject it. The null hypothesis is is called “null” because it is the “nothing” hypothesis, the result if no new information is gained in the experiment. The null hypothesis is formulated to reflect the current state of knowledge (or currently accepted version of truth).

What is the result, when null is compared with null?

Therefore NULL is neither equal to a value nor unequal to it, so any comparison involving NULL is neither true nor false. The result of a comparison involving NULL is not a boolean value -it is a non-value. You just can’t compare something that exists with something that doesn’t exist.