Please help me answer the question

1. Explain the difference between a Type I error and a Type II error? Type I error, also known as the level of significance, is when the researcher has rejected the null hypothesis when it is TRUE. The null hypothesis states that there is no difference. The significance level is the risk associated with not being 100% confident that what is observed in an experiment is due to the treatment and or what is being tested (performance-enhancing drug use among elite athletes). Type II error occurs when the researcher has accepted a false null hypothesis which states that there is no difference Then, imagine a table that illustrates the relationship between the nature of the null hypothesis and your action as a researcher (which includes potential Type I and Type II errors), and apply the four possible outcomes to situations of mandated drug testing in elite athletics. (0.5 point, 8-10 minutes) To imagine the table with different types of errors (Salkind, 2017, p 225). I developed a null hypothesis to analyze the subsequent statements. Ho. There is no difference between the sample mean for elite athlete performance enhancing drug use and the population mean for elite athlete performance-enhancing drug use Ho: X elite athlete performance-enhancing drug use equals M elite athlete performance-enhancing drug, a. Describe the statistical equivalent to when a famous baseball player's blood test was negative for performance enhancing drugs when there truly were no performance enhancing drugs present in his blood. There is NO difference between the groups 2-Oops / Type I error If the researcher rejects a true null hypothesis in this instance, then a Type I error has occurred. Researchers can never attain 100% confidence that that testing a sample reflects the population in every instance (Salkind, 2017. p. 224) b. Describe the statistical equivalent to when a world-class sprinter's blood test was positive for performance-enhancing drugs when there truly were no performance enhancing drugs present in her blood. There is a difference between the groups. 3- Uh oh In this instance, a Type II error occurs when the researcher fails to reject a false null hypothesis. There is a difference between the variables when a world-class sprinter's blood test was positive for performance-enhancing drugs when there truly were no performance enhancing drugs present in her blood Thus, the results provided evidence that researchers can never be 100% confident that testing the sample reflects the profile of the population in every single aspect (Salkind, 2017. p. 224). Also, researchers Page 2 of 4 1950 words Type here to search O w