Which statement is a testable hypothesis?
A testable hypothesis is a hypothesis that can be proved or disproved as a result of testing, data collection, or experience. Only testable hypotheses can be used to conceive and perform an experiment using the scientific method.
What is a testable hypothesis mean?
For a hypothesis to be testable means that it is possible to make observations that agree or disagree with it. If a hypothesis cannot be tested by making observations, it is not scientific. Given the nature of the hypothesis, there are no observations a scientist could make to test whether or not it is false.
What is a valid scientific hypothesis?
A hypothesis is an “educated guess.” It can be an educated guess about what nature is going to do, or about why nature does what it does. A scientific hypothesis must meet 2 requirements: A scientific hypothesis must be testable, and; A scientific hypothesis must be falsifiable.
Why are null and alternative hypothesis important?
The purpose and importance of the null hypothesis and alternative hypothesis are that they provide an approximate description of the phenomena. The purpose is to provide the researcher or an investigator with a relational statement that is directly tested in a research study.
Can you prove an alternative hypothesis?
When a predetermined number of subjects in a hypothesis test prove the “alternative hypothesis,” then the original hypothesis (the “null hypothesis”) is overturned or “rejected.” You must decide the level of statistical significance in your hypothesis, as you can never be 100 percent confident in your findings.
Can you reject the null and 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. You should note that you cannot accept the null hypothesis, but only find evidence against it.
Do you ever reject the alternative hypothesis?
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. When your p-value is greater than your significance level, you fail to reject the null hypothesis.