Final answer:
Without access to specific details from Wasserman's data, it's impossible to assert which scenario is more common between case 1 or case 2. Generally, statistical significance tests or frequency distribution analysis would be utilized to make such a determination.
Step-by-step explanation:
The student is asking for a comparison based on Wasserman's data to determine which scenario is more common: case 1 or case 2. However, it's impossible to provide an accurate answer without specific details from Wasserman's data or context. Generally, to determine whether one scenario is more common or more effective than another in statistics, one would use hypothesis testing or analyze the frequency distribution of the data.
For example, if considering Wax 1's effectiveness in comparison to Wax 2, one would use a significance test at a 5 percent level to determine if there is a statistically significant difference between the two waxes' performance. Concerning the errors mentioned, a Type I error occurs when we mistakenly reject the true null hypothesis, while a Type II error occurs when we fail to reject a false null hypothesis. This framework would help to address whether more or less than 60 percent of Americans vote in presidential elections or the percentage of adults riding the bus to work in Los Angeles.
Comparing scenarios or evaluating the likelihood of certain outcomes in statistics requires a methodology that includes setting up control groups, determining confidence levels, and calculating probabilities. Without these, it's not feasible to answer which scenario between case 1 and case 2 is more common based on Wasserman's data.