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To be more sure of their results. Say you are testing the effects of aspirin on Heart disease. If you only tested two people (one that got aspirin and one that got a placebo) there are an enormous number of reasons why the two people could be different BESIDES getting aspirin or placebo. If you tested 1000 that got aspirin and 1000 that got placebo and the two groups differed, it should be because of the aspirin (the other possible explanations should cancel-out in such a large sample, why would all the people with the worst heart health or all the men end-up in one group?). The larger the "sample size" the more sure we will be that the effect was actually due to the aspirin. This also enables us to look not at the effect of aspirin on an individual (something that is very unstable and varies a lot from person to person) but the effect of aspirin on a GROUP. An effect that is far more stable. In general, the more variable the effects, the larger the sample you will need. Physics and Chemistry usually deal with very stable effects, so they need a small sample. Psychology, Sociology and other Social Sciences usually deal with very variable effects, so they need LOTS of people to be sure the effects they see are real. In general, the larger the sample is, the more accurate our results are. Large samples also ensure a normal distribution and other advantages for statistical testing, but these samples must be RANDOM samples or they do not mean what we think they do.

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