What does it mean when you retain the null hypothesis?
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What does it mean when you retain the null hypothesis?
If our decision is to RETAIN the Null Hypothesis (H0), we are concluding that there is NO significant difference between (among) groups. If our decision is to REJECT the Null Hypothesis (H0), we are concluding that there is a significant difference between (among) groups.
Can we ever really know for sure that the null hypothesis is false?
From our hypothesis test, we therefore choose either to accept or to reject the null hypothesis. This is a critical point: regardless of the results of our statistical test, we will never know if the null hypothesis is true or false.
When making the decision to retain or reject a hypothesis which hypothesis are we making this decision on?
null hypothesis
When the p value is greater than 5\% (p > . 05), we retain the null hypothesis. The decision to reject or retain the null hypothesis is called significance.
What is the importance of assuming the null hypothesis is true?
The logic of null hypothesis testing involves assuming that the null hypothesis is true, finding how likely the sample result would be if this assumption were correct, and then making a decision.
What are three factors that affect the decision about null hypothesis?
The power of a hypothesis test is affected by three factors.
- Sample size (n). Other things being equal, the greater the sample size, the greater the power of the test.
- Significance level (α). The lower the significance level, the lower the power of the test.
- The “true” value of the parameter being tested.
Is it possible that a false hypothesis will be accepted?
A type II error does not reject the null hypothesis, even though the alternative hypothesis is the true state of nature. In other words, a false finding is accepted as true.
Is it possible that a false hypothesis will be accepted explain?
Ideally alpha and beta errors would be set at zero, eliminating the possibility of false-positive and false-negative results. In practice they are made as small as possible….Table 2.
| Truth in the population | Association + nt | No association |
|---|---|---|
| Fail to reject null hypothesis | Type II error | Correct |
What type of error is occurred in decision making when the true hypothesis is rejected?
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. The error rejects the alternative hypothesis, even though it does not occur due to chance.
What type of error occurs when a researcher rejects a null hypothesis that is true?
A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.
Is the ability to reject the null hypothesis when the null hypothesis is actually false?
Power is the probability of making a correct decision (to reject the null hypothesis) when the null hypothesis is false. Power is the probability that a test of significance will pick up on an effect that is present. Power is the probability of avoiding a Type II error.