Data analysis: hypothesis testing

Introduction

This course covers the fundamental principles of developing hypotheses. You will learn to distinguish between hypotheses that are directional and non-directional. This knowledge is crucial for the statistical testing that you will learn to implement.

You will also explore z-tests and t-tests, two fundamental statistical tools essential for data analysis in business contexts. By understanding the differences between these tests and learning how to apply them effectively, you will develop the skills to assess data-driven claims with confidence.

Figure 1 Data sheds light on hypotheses

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The picture portrays a professor conducting an open lecture on the moon, with a tag line displayed at the bottom which states, ‘Surprisingly, the soil samples provide new evidence for the “giant ball of cheese” hypothesis.’

Figure 1 Data sheds light on hypotheses

The course will focus on the characteristics and applications of z-tests and t-tests. You will learn when to use each test based on sample size, population parameters, and the nature of the data. This knowledge will enable you to choose the appropriate test for various business scenarios, ensuring accurate analysis and interpretation of results.

To reinforce your learning, you will encounter practical examples and exercises throughout the materials. These hands-on activities will allow you to practice applying z-tests and t-tests to real-world business problems, helping you to solidify your understanding and gain confidence in using these statistical methods

Please note: this course will require the use Microsoft Excel or a similar program.

This OpenLearn course is an adapted extract from the Open University course B126 Business data analytics and decision making [Tip: hold Ctrl and click a link to open it in a new tab. (Hide tip)] .