Suppose a scientist studying climate change compares estimates of the average gl
ID: 2958073 • Letter: S
Question
Suppose a scientist studying climate change compares estimates of the average global temperature in the 1790’s to the average global temperature in the 1990’s. He estimates that the average global temperature in the 1790’s was 56 degrees based on a data set of 30 temperature measurements having a standard deviation of 5 degrees. He estimates that the average global temperature in the 1990’s was 57 degrees based on a data set of 60 temperature measurements having a standard deviation of 2 degrees.a) Describe a Type-1 and Type-2 error in this experiment.
Type I error
Type 2 error
b) Based on your results from part a, which type of error might you be making.
Explanation / Answer
In statistics, a Type I error is when you reject a null hypothesis and it is true. A Type II error is when you don't reject the null hypothesis, and it is false. In this case, the scientist has two hypotheses: the null and alternative. The null hypothesis is what he is trying to prove or disprove, so in this case: Null: There is no difference in global temperature between the 1790's and 1990's. Alternative: There has been a change in global temperature since the 1790's and 1990's. So, to make a Type I error would be to claim that there was change in global temperature when there was not. To make a Type II error is to claim there was no change when in reality there was. Since he's coming to the conclusion that there HAS been change (as in, the null hypothesis is false), then the type of error he would be making if he were incorrect would be Type I. If he were claiming there WAS no change, when there had been, it would be a type II.
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