A systematic procedure for deciding whether the results of a research study support a particular theory which applies to a population
Hypothesis testing
A systematic procedure for deciding whether the results of a research study support a particular theory which applies to a population is known as hypothesis testing or inferential statistics.
Hypothesis testing involves the following steps:
1. Formulate a hypothesis- This involves defining the research question and creating a clear statement of the relationship between the variables under investigation.
2. Set the significance level – This involves setting the level of significance that specifies the threshold for rejecting the null hypothesis.
3. Collect data – The next step is to collect data using a research design and methodology that ensures that the data gathered is reliable and valid.
4. Analyze data – The collected data is then analyzed using statistical tests that help to quantify the relationships between variables and identify patterns and trends.
5. Interpret the results – Once the data is analyzed, the results are interpreted in the context of the research question and hypothesis.
6. Draw conclusions – Finally, conclusions are drawn based on the results of the hypothesis testing, and the researcher determines whether the results support or contradict the research hypothesis.
It is important to note that statistical significance does not imply practical significance, and a statistically significant relationship does not necessarily mean that the relationship between the variables is strong or meaningful in real-world terms. Therefore, it is crucial to interpret the results of the hypothesis testing in the context of the research question and the existing body of knowledge in the research area.
More Answers:
Hypothesis Testing: The Significance Of Null Hypothesis And Alternative Hypothesis In Comparing Populations Or VariablesP-Values: Calculating The Evidence Against Null Hypothesis And For Alternative Hypothesis
Hypothesis Testing: Comparing Sample Values To Critical Values In T-Tests And Chi-Squared Tests