Final answer:
The probability of making a Type I error based on a particular set of data is called the level of significance in hypothesis testing.
Step-by-step explanation:
The probability of making a Type I error based on a particular set of data is called the level of significance in hypothesis testing. It is denoted by the symbol 'a' and represents the probability of rejecting the null hypothesis when it is true. A Type I error occurs when a true null hypothesis is incorrectly rejected.
The level of significance is often set at a certain threshold, such as 5% (0.05). This means that if the calculated probability of obtaining the observed data under the null hypothesis is less than 5%, the null hypothesis is rejected and a Type I error is made.
For example, if a researcher is testing a new drug and sets the level of significance at 0.05, and the calculated p-value (probability) is 0.02, then the null hypothesis is rejected at the 5% significance level, indicating a Type I error could have occurred.