Quick Answer: How Should You Interpret A Decision That Rejects The Null Hypothesis Quizlet?

What type of error is made if you reject the null hypothesis when the null hypothesis is actually true?

If we reject the null hypothesis when it is true, then we made a type I error.

If the null hypothesis is false and we failed to reject it, we made another error called a Type II error..

Do you reject or fail to reject h0 at the 0.01 level of significance?

Rejecting or failing to reject the null hypothesis 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.

What are the two decisions that you can make from performing a hypothesis test?

What are the two decisions that you can make from performing a hypothesis​ test? Determine whether the statement is true or false. If it is​ false, rewrite it as a true statement. In a hypothesis​ test, you assume the alternative hypothesis is true.

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

If a hypothesis test is performed, how should you interpret a decision that fails to reject the null hypothesis? There is sufficient evidence to support the claim μ > 57.4.

What does rejection of null hypothesis mean?

Basically, you reject the null hypothesis when your test value falls into the rejection region. There are four main ways you’ll compute test values and either support or reject your null hypothesis.

What type of error occurs when a false null hypothesis is not rejected?

Type II error is the error made when the null hypothesis is not rejected when in fact the alternative hypothesis is true. The probability of rejecting false null hypothesis.

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.

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

In statistical analysis, a type I error is the rejection of a true null hypothesis, whereas a type II error describes the error that occurs when one fails to reject a null hypothesis that is actually false.

What should you conclude if your p-value is greater than the level of significance alpha )? Quizlet?

If the p-value is less than alpha (p , . 05), then we reject the null hypothesis, and we say the result is statistically significant. If the p-value is greater than alpha (p , . 05), then we fail to reject the null hypothesis, and we say the result is statistically nonsignificant.

How should you interpret a decision that rejects the null hypothesis?

Interpret the decision in the context of the original claim. If the claim is the null hypothesis and H₀ is​ rejected, then there is enough evidence to reject the claim. If H₀ is not​ rejected, then there is not enough evidence to reject the claim.

Why do we say fail to reject?

Failing to Reject vs. However, if the data does not support the alternative hypothesis, this does not mean that the null hypothesis is true. All it means is that the null hypothesis has not been disproven—hence the term “failure to reject.” A “failure to reject” a hypothesis should not be confused with acceptance.

Which of the following is the probability of failing to reject a false null hypothesis?

Failing to reject the null hypothesis when it is false is called a Type 2 error. … Thus, the probability of rejecting the null and making the correct decision when there is an effect is 1 – β, called the power of the test.

What type of error is made when a true null hypothesis is rejected quizlet?

A Type I error is committed when we reject a null hypothesis that is, in reality, true. A Type II error is committed when we fail to reject a null hypothesis that is, in reality, not true. The value of α is the probability of committing a Type I error.

How do you know when to reject or fail to reject the null hypothesis?

When your p-value is less than or equal to your significance level, you reject the null hypothesis. … Your results are statistically significant. When your p-value is greater than your significance level, you fail to reject the null hypothesis.

When we reject the null hypothesis we are saying that?

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 .

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.

How do you use the P-value to reject the null hypothesis?

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. That’s pretty straightforward, right? Below 0.05, significant.

Why do we say we fail to reject the null hypothesis instead of we accept the null hypothesis?

If the P-value is greater than the significance level, we say we “fail to reject” the null hypothesis. We never say that we “accept” the null hypothesis. We just say that we don’t have enough evidence to reject it. This is equivalent to saying we don’t have enough evidence to support the alternative hypothesis.

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