Date: 09/27/2001 at 23:28:00 From: Kendra Subject: What is this question asking me? Suppose that insted of choosing random samples of 25 students from a population of 100 students, you selected the first 25 students for the first sample, the next 25 students for the second sample, and so on. How might this sampling procedure bias the statistical results? Thank you for your time. Kendra
Date: 09/29/2001 at 23:36:04 From: Doctor Wolfson Subject: Re: What is this question asking me? Hi Kendra, Let me ask you a similar question that might help clarify it. Let's say that I ask you to find out how much food a bear eats during the course of a year. I ask you to take three friends with you to study the bear for a year, and report back to me one year from today, so I can compare your results. There are (at least!) two ways you could go about studying the bear with your friends. In the first way, each morning each of you flips a coin twice in a row. If you get two Heads, then you'll watch the bear for the day. Otherwise, you'll take the day off. Sometimes none of you will get two heads and watch the bear, and sometimes more than one of you will be watching at once. But in the end, you'll each have a pretty good idea of the bear's eating habits. In the second way, you decide that you will be the one to watch the bear for the first three months, and write your report so you can relax for the rest of the year. Then each of your friends will take a three-month turn watching the bear and writing a report. In a year when you report back to me, will the four reports look similar? Well, your report (the first one, during the autumn) will say that the bear eats an incredible amount of food, and that if he kept eating like that he should be much larger than he really is. Your next friend who watched the bear during the winter will tell me that the bear spent the whole time sleeping and didn't eat at all! If bears never eat, how do they get so big? Of course the reason that you and your friend got such different answers is that there are patterns in the bear's eating habits; during the fall, he eats a lot to store up energy to hibernate for the winter. But it would be a mistake to take this trend and assume that it applies all year long. And if one of the reports (yours) is higher than the true average amount the bear eats, then at least one of them has to be lower, to keep things in balance. That's why your winter friend never got to see the bear eat. And, most likely, the last two friends saw relatively normal eating habits. Can you see how this relates to your question? If you don't pick a random sample, then any patterns that you didn't account for in your sample can skew the data. And even worse, if the samples don't overlap, then each skew will be balanced by a counter-skew; in other words, if one sample contains a disproportionately large amount of something, then another one has to offset this and be too small. (An "average" can't have all the data above it, so if there's an "above average," there has to be a "below average.") I hope this helps. Feel free to write back if you'd like more clarification. - Doctor Wolfson, The Math Forum http://mathforum.org/dr.math/
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