Just answer true or false, thanks! 2. (36 pts) Answer the following True-False q
ID: 3182704 • Letter: J
Question
Just answer true or false, thanks!
2. (36 pts) Answer the following True-False questions. In your write-up, just list the sub-question letter (A-R) and whether the statement is True or False – no need to restate the question or to justify your answer.
If the number of trials in the binomial distribution increases by 1 (and P equals .50), the probability of getting either of the most extreme possible outcomes (that is, 0 or N) is cut in half.
If the number of trials in the binomial distribution increases by 1 (and P does not equal .50), the probability of getting either of the most extreme possible outcomes (that is, 0 or N) is cut in half.
In the binomial distribution, as the probability of a “+” or “-” outcome differs more and more from .5, the shape of the distribution of probabilities for all possible outcomes always becomes more and more symmetrical.
In the sign test, the p value associated with a given number of “+” outcomes will be the same as the p value associated with the same number of “-” outcomes, when the test is two-tailed.
In the sign test, when the obtained result is exactly what would be expected by chance (like 9 successes out of 18 trials when P = .50), the p value (assuming you’re doing a two-tailed test) can sometimes be greater than 1.00.
In the sign test, if N increases from 15 to 20 and alpha is made less stringent (like from .01 to .05), the number of distinct possible outcomes (e.g., number of heads out of N) that allow rejection of H0 must decrease.
In the sign test, if N decreases and the size of the effect of the independent variable decreases in strength, the probability of a Type II error decreases.
In the sign test, as the numerical value of Preal decreases, the power of an experiment must always decrease.
As power decreases, the probability of correctly rejecting the null hypothesis increases.
If a researcher fails to reject the null hypothesis, then she must “accept” the null hypothesis.
A researcher will always know for sure when she has made a Type I error
If the obtained p value is less than the alpha level, the null hypothesis should be rejected.
It is impossible to make a Type II error when you reject the null hypothesis.
If a researcher uses a one-tailed test, it will be easier for her to reject the null hypothesis than if she uses a two-tailed test, assuming the effect is in the predicted direction.
If a researcher uses a one-tailed test, it will be easier for her to reject the null hypothesis than if she uses a two-tailed test, even if the effect is in not the predicted direction.
All else being equal, if the N in the sign test decreases, it becomes harder to reject the null hypothesis.
15 trials, increasing the alpha level from .01 to .05 means that fewer of the possible particular outcomes (like number of trials correct) will allow rejection of the null hypothesis.
All else being equal, if a researcher increases the likelihood of making a Type I error by increasing the value of alpha, then she is also more likely to make a Type II error.
Explanation / Answer
2. Solution:
If the number of trials in the binomial distribution increases by 1 (and P equals .50), the probability of getting either of the most extreme possible outcomes (that is, 0 or N) is cut in half.
TRUE
=>If p = 0.5 is 0.5^n. Hence, if n increases by 1, then it becomes 0.5^(n+1) = 0.5^n (0.5). So, it is multiplied by 0.5, so it is cut in half.
If the number of trials in the binomial distribution increases by 1 (and P does not equal .50), the probability of getting either of the most extreme possible outcomes (that is, 0 or N) is cut in half.
FALSE
=>It becomes more asymetrical as p becomes farther from p = 0.5
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