How To Solve A Hypothesis Testing Statistics Problem

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The first type of error occurs when the null hypothesis is wrongly rejected.

The second type of error occurs when the null hypothesis is wrongly not rejected.

Each lecturer has 50 statistics students who are studying a graduate degree in management.

In Sarah's class, students have to attend one lecture and one seminar class every week, whilst in Mike's class students only have to attend one lecture.

This leads to the following research hypothesis: Before moving onto the second step of the hypothesis testing process, we need to take you on a brief detour to explain why you need to run hypothesis testing at all. If you have measured individuals (or any other type of "object") in a study and want to understand differences (or any other type of effect), you can simply summarize the data you have collected.

8d Problem Solving Methodology - How To Solve A Hypothesis Testing Statistics Problem

For example, if Sarah and Mike wanted to know which teaching method was the best, they could simply compare the performance achieved by the two groups of students – the group of students that took lectures and seminar classes, and the group of students that took lectures by themselves – and conclude that the best method was the teaching method which resulted in the highest performance.The former process was advantageous in the past when only tables of test statistics at common probability thresholds were available.It allowed a decision to be made without the calculation of a probability.(The two types are known as type 1 and type 2 errors.) Hypothesis tests based on statistical significance are another way of expressing confidence intervals (more precisely, confidence sets).In other words, every hypothesis test based on significance can be obtained via a confidence interval, and every confidence interval can be obtained via a hypothesis test based on significance.However, in order to use hypothesis testing, you need to re-state your research hypothesis as a null and alternative hypothesis.Before you can do this, it is best to consider the process/structure involved in hypothesis testing and what you are measuring. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis that proposes no relationship between two data sets.The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level.However, this is generally of only limited appeal because the conclusions could only apply to students in this study.However, if those students were representative of all statistics students on a graduate management degree, the study would have wider appeal.


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