However, if we are analyzing humans or companies, we would expect more random or at least uncertain behavior. So, in certain situations, we need to be as accurate as possible. If the machine pours 12.1 ounces, some of the liquid would be spilled, and the label would be damaged as well. The famous Coca Cola glass bottle is 12 ounces. As we want to be very precise, we should pick a low significance level such as 0.01. We would expect the test to make little or no mistakes. Say, we need to test if a machine is working properly. In most cases, the choice of α is determined by the context we are operating in, but 0.05 is the most commonly used value. It is a value that we select based on the certainty we need. Typical values for α are 0.01, 0.05 and 0.1. So, the probability of making this error. The significance level is denoted by α and is the probability of rejecting the null hypothesis, if it is true. However, as with any test, there is a small chance that we could get it wrong and reject a null hypothesis that is true. Normally, we aim to reject the null if it is false. What Is the Significance Level?įirst, we must define the term significance level. We assume you already know what a hypothesis is, so let’s jump right into the action. ![]() If you want to understand why hypothesis testing works, you should first have an idea about the significance level and the reject region.
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