Question

1) what does it mean if a psychological scientist runs an experiment and finds a statistically significant result? a) The likelihood of a Type I error is greater than 5% b) The likelihood that the result was due to chance is low enough to reject the null hypothesis c) The theory that the scientist was testing is proven

2) What decision must a psychological scientist make if an obtained p-value is greater than the adopted alpha? a) To accept the null hypothesis b) To reject the null hypothesis c) That there is a type I error

3) What does a psychological scientist conclude if an obtained p-value is less than the adopted alpha? a) The likelihood that the result was due to chance is too high to reject the null hypothesis b) The effect of the IV manipulation is statistically significant c) The likelihood of a type II error is greater than 5%

4) With all else being equal, what happens to the inferential stat. we calculate to determine whether 2 groups differ, as the difference between their means increases? a) the Pearson's r increases b) The t-score increase c) The variance decreases d) The sum of squares decrease

5) All else being equal, what happens to the inferential stat. we calculate to determine whether 2 groups differ, as the variance of each of the groups increases? a) The Pearson's r decreases b) The t-score decreases c) The mean increases d)The sum of squares increases

6) All else being equal, what happens to the p-value that corresponds with our inferential stat., as the difference between the means of two groups increases? a) it does not change b) It increases c) It decreases d) it approaches 1.0

Answer 1

1)...The theory that the scientist was testing is proven... Because a statistically significant result rejects the null hypothesis of no difference... A psychological scientist always would like to prove some theory which can always be a part of alternate hypothesis.

2)If pvalue is greater than the adopted alpha then we would normally do not reject the null hypothesis because the rejection would lead to increase the chance of type I error..

3) we Reject the null hypothesis. The effect of IV manupulation is statistically significant. Type II error has no role here in decideding whether to reject a hypothesis or not. .. As we would have been using the most powerful test for proving the hypothesis... Type Ii error is always low and plays very little role in decision making.

4..The t score increases which leads rejection of null hypothesis

5. THE T SCORE decreases as the variance increases... The variance falls in the denomimator.. T score tends to be reduced

6.. As the pvalue is based on test statistic value... It gets affected by increasing the difference between means. By increasing the difference between means with all the other values kept equal.. The t score increases which leads to rejection of null hypothesis... So pvalue which itself is the probability of type i error will be tending to zero or less than the level of significance considered..