Hypothesis testing is critical for the analysis of clinical trials, and its techniques have useful applications long after the completion of a trial. Whether you are starting a trial and need to formulate benchmarks, or setting a standard for measuring Adverse Event (AE) data throughout the life-cycle management phase, hypothesis testing is the foundational tool necessary to produce accurate, scientifically-sound data.
This interactive, 120-minute session will give you the tools to set-up your hypotheses, identify the correct test to use, complete the test, and make your decision on the hypothesis in question. Additionally, the course will aid you in turning your statistical data into an answer that will make sense in the “real world.” You will learn about a and b risks plus different types of tests, such as the z-test and t-test, and what a p-value is. Additionally, we will talk about how you use a p-value to make your decision. Furthermore, you will develop the necessary skills to employ these hypothesis testing techniques in your applications. Course content will be presented with pertinent examples including valid conclusions.
Come to the presentation and you will be able to:
- Develop a foundational understanding of principles of hypothesis testing
- Use software to run the analysis and get pertinent information to make your decision based on the data (we will be using the free, open-source rcommander software)
- Understand which type of test should be used for a given situation
- Understand how to interpret the results
- Understand how to convert the statistical results into real-world conclusions