Hi, Maryann. From my own work with high school teachers, I have found that the best entry point for simulation-based inference is to introduce them to two cases that are pretty accessible: 1. Using simulation to test a claim about a population proportion based on a random sample from that population. Just simulate many, many samples of that size under the assumption that the claim is true and record the value of the sample proportion for each one in a dotplot. Then look where the observed result falls in the simulated sampling distribution, and ask whether the sample result is sufficiently surprising (far out in the tails of the distribution) to provide convincing evidence against the claim. Ideally, we'd have learners do this with a spinner or some other physical device first before proceeding to technology, which would necessitate using a fairly small sample size for practical reasons. 2. Using simulation to determine whether the difference between two proportions is statistically significant in a randomized experiment. Assume that there is no difference in the effects of the two treatments on the subjects in the study (null hypothesis). Simulate re-doing the random assignment of subjects to treatments many, many times, keeping each subject's response (success or failure) the same as it was in the original experiment. Each time, record the difference in proportions of successes for the two groups on a dotplot. Then look where the observed result falls in the simulated randomization distribution, and ask whether the observed difference in proportions is sufficiently surprising (far out in the tails of the distribution) to provide convincing evidence against the null hypothesis. Ideally, we'd have learners do this with by shuffling and dealing cards or some other physical device first before proceeding to technology, which would necessitate using fairly small group sizes for practical reasons. There are great resources available from several members of this list that could be used as the basis for these two distinct activities that would introduce teachers to the different scope of inference for random sampling and randomized experiments. Daren Starnes ********************************************************************************************** Hello All, As person who spends most of her summer working with high school teachers on stats and probability content and creating lesson plans, which are used in the next school year, I've followed this discussion eagerly. High school teachers are relatively easily convinced that a large enough, random sample is usually representative of the population. Convincing teachers that one of these samples could be used to mimic the entire population and then be utilized to generate more random samples is quite a different thing. I am convinced of the bootstrapping process, but to leap there immediately with teachers versus the more cumbersome routes discussed in this chain of responses might cause serious distress. Are there resources to help educate high school teachers (and myself further) in regard to bootstrapping? Research and experience shows that teachers with either omit or superficially enact contact that they feel is beyond their current knowledge base. Simulation, in general, has been daunting for high school teachers. Of 23 we worked with last summer, only 25% took the plunge with re-randomization. However, the ones that did, thoroughly enjoyed the experience, as did their students. Best, Maryann *----------------------------------------* *Maryann E. Huey* *Mathematics and Computer Science* *Drake University* *515/271-2839 <515%2F271-2839>*