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Data analysis: hypothesis testing
Making decisions about the world based on data requires a process that bridges the gap between unstructured data and the decision. Statistical hypothesis testing helps decision-making by formulating beliefs about the world, including people, organisations or other objects, and formally testing these beliefs.
In this free course, you will study the principles of hypothesis testing, including the specification of significance levels, as well as one-sided and two-sided tests. Finally, you will learn how to perform a hypothesis test of the mean of a variable, as well as the proportion of individuals in a dataset with a certain characteristic.
You will use spreadsheets throughout the course as the central tool used by professionals for simple data management and analysis.
This OpenLearn course is an adapted extract from the Open University course B126 Business data analytics and decision making.
Course learning outcomes
After studying this course, you should be able to:
- understand the principle of hypothesis testing
- understand the idea of alpha in hypothesis testing
- differentiate between one-tailed and two-tailed tests
- understand hypothesis testing of means and proportions
- report the exact p-value of a test.
First Published: 02/01/2024
Updated: 02/01/2024
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However, there were 2 variants in 5.2 that I was unclear about, e.g., in terms of the earlier statement in 5.1:
"The two-tailed test requires you to divide the levels of alpha by 2.
...
Using the z-score table, you can determine the z-score = 1.96 or -1.96. Therefore, you will reject the null hypothesis for obtained z-score > 1.96 or z-score < 1.96. "
[...actually, should that be "< -1.96"?]
5.2 Test your understanding
Table 3: the two-tailed test results state, e.g., “Area LEFT of z: X AND Y”?