Introduction to Inferential Statistics and SPSS-TOPIC 3 DQ 1

Introduction to Inferential Statistics and SPSS-TOPIC 3 DQ 1


QUESTION- Summarize the six steps of hypothesis testing. Propose a scenario in which hypothesis testing is applied to public health data.

CLASSMATE RESPONSE-Classmate (Eleojo Karfe )Response –
A hypothesis, in statistics, is a statement about a population parameter, where this statement typically is represented by some specific numerical value. In testing an idea, we use a method to gather data to gather evidence about the hypothesis. The six steps of the hypothesis include
Step 1: hypothesis testing is selecting which test to use. Each hypothesis test includes two hypotheses about the population. There are hundreds of statistical tests, each designed for a specific purpose. Choosing the correct test depends on various factors, such as the question asked, the type of study done, and the level of measurement of the data.
Step 2: Check the Assumptions. All statistical tests have assumptions, conditions that need to meet before a test is complete. If the belief is unsettled, researchers can’t be sure what the test results mean.
Step 3: List the Hypotheses. It entails listing the null hypothesis and the alternative hypothesis. Assumptions can be for two-tailed tests (also called non-directional tests) or one-tailed tests (directional tests). A two-tailed hypothesis test doesn’t indicate whether the explanatory variable (adoption in our example) has a positive or negative impact on the outcome variable (IQ), just that it affects.
Step 4: Set the Decision Rule. It entails finding the critical value of the test statistic. The critical value is the test statistic’s value to meet or exceed to reject the null hypothesis.
Step 5: Calculate the Test Statistic. Calculating the test statistic is the most straightforward of the six steps for hypothesis testing. First, plug the correct numbers into a formula and push the right buttons in the proper order on the calculator: that’s how to calculate the test statistic.
Step 6. Interpret the Results. Interpreting the results is the reason for doing statistical tests. The researcher explains, in plain language, what the results are and what they mean. Interpretation is a human skill. Researchers should summarize the results into an overall conclusion for the test.
A scenario in which hypothesis applied to public health setting would possibly evaluate the effectiveness of new diabetes study drug among minority population in the US. Conducting clinical research trials, screen, select, and divide study participants into two groups: those who take actual study drugs and those who take the placebo. The aim is to measure the safety and efficiency of this drug among the minority population and determine the cause of the increase in diabetes among this population which could be heredity or lifestyle.

Corty, E. (2016). Using and interpreting statistics is a practical text for the behavioral, social, and health sciences (pp. 79-84): essay, Worth Publishers/Macmillan Learning.

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Hypothesis Testing Response
In your post, you have listed the six steps of hypothesis testing that include selecting the hypothesis testing to use, checking the assumptions, listing the hypotheses, setting the decision rule, calculating the test statistics, and interpreting the results for conclusions. It is important to note that various assumptions can be made towards the hypothesis at the assumptions’ step, such as on the distribution of the underlying data, samples sizes and methods, measurement levels of data, sample characteristics, and the level of significance for the testing. The rejection region or the decision rule, as you have indicated, is important in guiding the calculations. According to Eberly College of Science (2020), the value of this step is to inform the reader no how the hypothesis test will be used in rejecting or failing to reject the null hypotheses, including the critical values for making the determination. The 5th step involves a calculation of the test statistics. It is important to include the various processes involved in effectively calculating the collected data for approving or disapproving the hypotheses. It is important to note that calculations should start by breaking down the problem into smaller parts to avoid calculation errors and enhance the calculated data’s conclusiveness (Rebekah & Ravindran, 2018). The statistician should also employ problem-solving art. After simplifying the data into sects, the statistician should construct a simple spreadsheet among other statistical tools solving numerous problems and relationships between the variables at once. In making the calculations and presentations, it is essential to maintain clarity, considering the audience. It is important to have communicative calculations before making conclusions.
The scenario you have provided in the application of the hypothesis testing is okay. Testing the effectiveness and safety of a medication in treating a particular disease against a placebo would provide a hypothetic comparison. The healthcare teams and medical experts use the hypothesis testing technique in evaluating the possible impact of a drug intervention. You may also include the null hypothesis, the alternative hypothesis, and the possible assumptions that can be made in such a study.

Eberly College of Science. (2020). 6a.2 – Steps for Hypothesis Tests | STAT 500. PennState: Statistics Online Courses.
Rebekah, G., & Ravindran, V. (2018). Statistical analysis in nursing research. Indian Journal of Continuing Nursing Education, 19(1), 62.;year=2018;volume=19;issue=1;spage=62;epage=70;aulast=Rebekah