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# 3 One-tailed vs Two-tailed test

Understanding how to conduct hypothesis tests is crucial in statistical analysis. This section explores the concepts of one-tailed and two-tailed tests, which are essential tools in statistical hypothesis testing. The choice between these tests depends on the specific research question and hypothesis under investigation. Decision-makers must carefully consider which type of test to use to ensure a thorough examination of the hypothesis and draw accurate conclusions from the data.

To deepen your understanding of these concepts, we will now engage in an activity focused on formulating null and alternative hypotheses. This exercise may present some challenges, but it serves as an excellent foundation for our subsequent discussions. Do not worry if you find it difficult initially – this is a common experience when learning these concepts.

## Activity 3: Hypotheses setting

Allow around 10 minutes for this activity.

Read the following statements and then develop a null hypothesis and an alternative hypothesis.

“It is believed that OU students need to set aside no longer than, on average, 15 hours to study an entire session of OU module. However, a decision-maker believes that OU students spend longer studying an entire session of the OU module”.

You may find this statement different from others you have experienced, so please take a longer time to think about it.

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### Discussion

H0: OU students spend, on average, no more than 15 hours studying an entire session of OU course.

Ha: OU students spend, on average, more than 15 hours studying an entire session of OU course.

They can also be written as:

H0: µ ≤ 15 hours studies

Ha: µ > 15 hours studies

µ is a symbol for a population mean. Remember, H0 and Ha are always opposites.

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OpenLearn - Data analysis: hypothesis testing
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