Learning Objectives Following this activity, students should be able to: Use the appropriate statistical test to evaluate the quality of data. Use the correct type of t test to make decisions regarding statistical significance. Compare the average and standard deviation of a data set to that of another data set. Compare the average and standard deviation of a data set to a “true” or accepted value. In the lab… Alice and Bill made repeated measurements of the amount of iron in two iron ore samples. The table below summarizes the results of their analyses. Alice Bill 20.14 % 20.02 % 20.33 % 20.08 % 20.20 % 19.98 % 20.26 % 20.08 % 20.23 % 20.15 % 20.13 % 20.06 % \(\bar{x}_A =\) \(\bar{x}_B =\) \(s_A =\) \(s_B =\) Statistical Analysis Use a 95% confidence level when needed. Should any of Alice’s and Bill’s data be discarded as statistical outliers? After discarding any “bad” data, find the mean and confidence interval for each set of data. Compare the precision of Alice’s and Bill’s results. Do they have similar levels of random error? Compare the means of the two data sets. Based on their results, do the two samples have different iron content? Alice and Bill learn their samples are actually from the same source that has an accepted value of 20.09% Fe. Are Alice’s and Bill’s means significantly different from this accepted value? Based on the results of your statistical analysis, do you think either Alice or Bob has systematic error in their analysis? Discuss some potential sources of systematic error that may arise in this analysis.