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.

• 1 Two types of hypotheses
• 1.1 Formulating null and alternative hypotheses
• 1.2 Population mean (µ)
• 2 Testing with data
• 3 Alpha (α) levels
• 4 One-tailed vs two-tailed test
• 4.1 The normal distribution
• 4.2 Two-tailed tests
• 4.3 One-sided tests
• 4.4 Check your understanding
• 5 Mean and z-score ranges
• 5.1 Acceptance and rejection regions
• 5.2 Test your understanding
• 5.3 Using the z-score
• 6 P-value
• 6.1 Defining the p-value
• 6.2 Calculating the p-value
• 6.3 Example: testing a hypothesis
• 6.4 Test your understanding
• 7 Hypothesis testing for population proportions
• 7.1 Example: testing a proportion
• 7.2 Test your understanding