David wants to complete a hypothesis test with the least amount of probability for error. If he sets the significance level to 1
%, assuming his sample is truly random, what else could he adjust in the test in order to reduce error? He could change the population mean. He could increase the sample size. He could change the population standard deviation. He could decrease the sample size.
In hypothesis testing, the error associated with the test is affected by a number of factors. The first factor is the level of significance, alpha. This is the probability of type 1 error. The probability of rejecting the null hypothesis when it is indeed true.
The second factor is the size of the sample used. The larger the sample, the smaller the error since the characteristics of the sample will be closer to those of the entire population on which inference is being made
