Table of Contents
- 1 What does having evidence to support the alternative hypothesis mean?
- 2 When we failed to reject the null hypothesis which of the following statements is true?
- 3 Does rejecting the null hypothesis means accepting the alternative hypothesis?
- 4 Is there enough evidence to reject the claim?
- 5 What is an example of a null hypothesis and alternative hypothesis?
What does having evidence to support the alternative hypothesis mean?
The alternative hypothesis states that a population parameter does not equal a specified value. Typically, this value is the null hypothesis value associated with no effect, such as zero. If your sample contains sufficient evidence, you can reject the null hypothesis and favor the alternative hypothesis.
How do you know if there is sufficient evidence to reject the null hypothesis?
Support or reject null hypothesis? If the P-value is less, reject the null hypothesis. If the P-value is more, keep the null hypothesis. 0.003 < 0.05, so we have enough evidence to reject the null hypothesis and accept the claim.
When we failed to reject the null hypothesis which of the following statements is true?
14 Answers. Failing to reject a null hypothesis is evidence that the null hypothesis is true, but it might not be particularly good evidence, and it certainly doesn’t prove the null hypothesis.
What is difference between null hypothesis and alternative hypothesis?
A null hypothesis is a statement, in which there is no relationship between two variables. An alternative hypothesis is statement in which there is some statistical significance between two measured phenomenon.
Does rejecting the null hypothesis means accepting the alternative hypothesis?
If the P-value is less than or equal to the significance level, we reject the null hypothesis and accept the alternative hypothesis instead. If the P-value is greater than the significance level, we say we “fail to reject” the null hypothesis.
When you incorrectly reject the null hypothesis you have created what type of error?
A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance.
Is there enough evidence to reject the claim?
The p-value is the probability of observing such a sample mean when the null hypothesis is true. 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.
When you reject the null hypothesis you have evidence that the difference observed is?
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 .
What is an example of a null hypothesis and alternative hypothesis?
Examples: Null Hypothesis: H0: There is no difference in the salary of factory workers based on gender. Alternative Hypothesis: Ha: Male factory workers have a higher salary than female factory workers.
How do you find the null and alternative hypothesis?
The actual test begins by considering two hypotheses. They are called the null hypothesis and the alternative hypothesis….Null and Alternative Hypotheses.
| H0 | Ha |
|---|---|
| equal (=) | not equal (≠) or greater than (>) or less than (<) |
| greater than or equal to (≥) | less than (<) |
| less than or equal to (≤) | more than (>) |