Table of Contents
When should a hypothesis be rejected?
We assume that the null hypothesis is correct until we have enough evidence to suggest otherwise. 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.
Can a valid hypothesis be rejected?
Upon analysis of the results, a hypothesis can be rejected or modified, but it can never be proven to be correct 100 percent of the time. For example, relativity has been tested many times, so it is generally accepted as true, but there could be an instance, which has not been encountered, where it is not true.
What causes a hypothesis to be rejected?
How low the p value must be before the sample result is considered unlikely in null hypothesis testing. When there is less than a 5\% chance of a result as extreme as the sample result occurring and the null hypothesis is rejected.
What happens if you reject the null hypothesis when it 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.
Can you accept alternative 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.
Is Alpha the critical value?
Critical values for a test of hypothesis depend upon a test statistic, which is specific to the type of test, and the significance level, \alpha, which defines the sensitivity of the test. A value of \alpha = 0.05 implies that the null hypothesis is rejected 5 \% of the time when it is in fact true.
What are the 5 steps of hypothesis testing?
Five Steps in Hypothesis Testing:
- Specify the Null Hypothesis.
- Specify the Alternative Hypothesis.
- Set the Significance Level (a)
- Calculate the Test Statistic and Corresponding P-Value.
- Drawing a Conclusion.
How do you know when there is enough evidence to support the claim?
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.
What is the null hypothesis in a hypothesis test?
A hypothesis test examines two opposing hypotheses about a population: the null hypothesis and the alternative hypothesis. The null hypothesis is the statement being tested. Usually, the null hypothesis is a statement of “no effect” or “no difference”. The alternative hypothesis is the statement you want to be able to conclude is true.
Why can’t we prove a hypothesis?
To conclude, you cannot prove a hypothesis because you can never generalise the results to the whole population and foresee the results will always be the same in the future. You can however, reject the null hypothesis consistently, through statistical hypothesis testing so that the theory becomes highly likely to be true, but not proven.
What are the two errors that can occur in hypothesis testing?
Correct Decision With respect to hypothesis testing the two errors that can occur are: (1) the null hypothesis is true but the decision based on the testing process is that the null hypothesis should be rejected, and (2) the null hypothesis is false but the testing process concludes that it should be accepted.
What is an example of a decision rule in hypothesis testing?
So, in hypothesis testing acceptance or rejection of the null hypothesis can be based on a decision rule. As an example of a decision rule, you might decide to reject the null hypothesis and accept the alternative hypothesis if 8 or more heads occur in 10 tosses of the coin. The process of testing hypotheses can be compared to court trials.