Type I and Type Ii Errors
Type I and II errors
Mistakenly rejecting the null hypothesis is a type 1 error. These errors are not avoidable and are part of statistical testing, but we can lessen the occurrence by setting the significance at a lower level. However, by setting the significance level lower; let us say .001, we then increase the chance of type 2 errors. Failing to correctly reject the null hypothesis creates a type 2 error, this is because; according to Aron (2009) “with an extreme significance level like .001, even if the research hypothesis is true, the results have to be quite strong for you to reject the null hypothesis.” To avoid type 2 errors, it is a good idea to increase the size of the experiment.
The sample size effects type 2 errors by determining the amount of sampling error. With small samples; effects are harder to detect, by increasing sample size we boost the statistical power of the test.
The selection of the alpha affects the type 2 test by causing the researcher to fail when rejecting the H0 if the alpha selection is incorrect. If the researcher is worried about the possibility of rejecting H0 when it is true, then a smaller alpha level like .01 or even .001 should be used. That would minimize the change that researcher would incorrectly reject H0.
Explain a factor that the researcher can control to change the type II error.
A researcher wants to find out how many times the red light will flash on an electronic Simon game. He begins an experiment and plays the game 4000 times and counts the number of red light flashes.
The null hypothesis is that the game is not set to a counting rotation, and the probability that the red light will flash is one out of four times. For this test the researcher will use a two tailed test because he has not indicated if he wants the true probability to be above or below 1/4.
The researcher will then decide the significance level of the test, (5% will be used in this scenario). In setting the significance level at 5%
Mistakenly rejecting the null hypothesis is a type 1 error. These errors are not avoidable and are part of statistical testing, but we can lessen the occurrence by setting the significance at a lower level. However, by setting the significance level lower; let us say .001, we then increase the chance of type 2 errors. Failing to correctly reject the null hypothesis creates a type 2 error, this is because; according to Aron (2009) “with an extreme significance level like .001, even if the research hypothesis is true, the results have to be quite strong for you to reject the null hypothesis.” To avoid type 2 errors, it is a good idea to increase the size of the experiment.
The sample size effects type 2 errors by determining the amount of sampling error. With small samples; effects are harder to detect, by increasing sample size we boost the statistical power of the test.
The selection of the alpha affects the type 2 test by causing the researcher to fail when rejecting the H0 if the alpha selection is incorrect. If the researcher is worried about the possibility of rejecting H0 when it is true, then a smaller alpha level like .01 or even .001 should be used. That would minimize the change that researcher would incorrectly reject H0.
Explain a factor that the researcher can control to change the type II error.
A researcher wants to find out how many times the red light will flash on an electronic Simon game. He begins an experiment and plays the game 4000 times and counts the number of red light flashes.
The null hypothesis is that the game is not set to a counting rotation, and the probability that the red light will flash is one out of four times. For this test the researcher will use a two tailed test because he has not indicated if he wants the true probability to be above or below 1/4.
The researcher will then decide the significance level of the test, (5% will be used in this scenario). In setting the significance level at 5%