This was done early in the trials, when there was limited data. An industry example of beta riskĭuring clinical trials of a new drug, a pharmaceutical company statistician did a hypothesis test to determine whether the new drug was more effective than the existing drug being used. This is the assumed risk you are willing to take for failing to reject the null and claim change when you should have. Since there is possible recovery from your bad decision, the beta risk is typically set between 15%-25%. The good news is, you can set your beta risk beforehand based on your risk profile. Once you discover your mistake, you may have a short-term cost, but nothing prevents you from implementing it in the future – although you will have lost potential sales, and your competition may have penetrated and stolen some of your market share. You now have an opportunity cost associated with failing to implement the new marketing program. Unfortunately, the truth was that it did increase sales. For example, when interpreting the results of your hypothesis test, you conclude the new marketing program was ineffective and did not increase sales. In the case of beta risk, you run the risks and consequences of inaction. Beta risk: Failing to conclude there was a change when there was is called a beta, or type 2 risk or error.Alpha risk: The risk of claiming there was change when there wasn’t is called an alpha, or type 1 risk or error.In both cases, you have a risk of making the wrong decision. Or you can decide not to reject the null and conclude there was no change. Once you have done the proper calculations, you must decide whether to reject the null and accept the alternate, concluding there was change. The second hypothesis, called the alternate or alternative hypothesis, states there was a meaningful change. The first, called the null hypothesis, states that there has been no statistically significant change in the process. This tool requires you to state two hypothesis statements. Hypothesis testing is the tool you would normally use. You collect data and now must make a decision of whether there was change or not. Now you must determine whether there actually was a statistically significant change. We will explore the concept of beta risk as it applies to doing hypothesis testing asl well as how you can reduce the risk and the consequences of being wrong. Making a decision based on sample data always has an inherent risk of making the wrong decision. Definition of Beta Risk: « Back to Glossary Index
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