1. As the effect size gets smaller, what is required so that the power of the te

Question

1. As the effect size gets smaller, what is required so that the power of the test remains constant? The sample size must decrease. The sample size must increase and the significance level must increase. The sample size must decrease and the significance level must increase. The sample size must increase. 2. All other factors being equal, which error probability would you choose to decrease? It is always a matter of judgment and context, so will be different for different data. 1 – 3. A “false positive,” in which an effect is detected (when in reality there is none), is an example of a Type II Error. None of the above the power of the test. a Type I Error. 4. If you are willing to reject the null hypothesis one time out of every 20 that you conduct the same test of significance, which of the following is always TRUE? The level of significance that you have set is = 0.025. The power of the test is too low. The level of significance that you have set is = 0.05. The effect size must be small for the power of the test to be high. 1. As the effect size gets smaller, what is required so that the power of the test remains constant? The sample size must decrease. The sample size must increase and the significance level must increase. The sample size must decrease and the significance level must increase. The sample size must increase.

Explanation / Answer

ans.1>  The sample size must increase and the significance level must increase.(option B)

Factors That Affect Power

The power of a hypothesis test is affected by three factors.

ans.2>

notice that here type I error should be minimized, otherwise innocent person goes to jail.

again sometimes type ii error should be minimized,(for example, type 2 error=alarm doesn't ring when there is a fire) so option A is correct that It is always a matter of judgment and context, so will be different for different data.

ans.3>

false positive is the type I error.

ans.4>

1 out of 20 times, so percentage is 5% out of 100%, so alfa=.05

Truth Not Guilty Guilty Verdict Guilty Type I Error -- Innocent person goes to jail (and maybe guilty person goes free) Correct Decision Not Guilty Correct Decision Type II Error -- Guilty person goes free

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