![]() On the other hand, if the research is more qualitative and aims to gather directional information for a product or brand, a large sample size is most likely not required and could in fact be detrimental.Īnother potential issue with obtaining large samples is the issue of statistical significance. If the aim of the research is to cut the data by examining differences between multiple "subgroups," a large sample is likely necessary. Large-scale, specifically targeted quantitative studies may require a large, robust sample, particularly if many targets or subsections of the population are of interest. Exactly what a representative sample is or what it looks like is completely dependent on your research objectives and the specific information you hope to learn from the research results. Makes perfect sense, right?Įxactly what a representative sample is or what it looks like is completely dependent on your research objectivesĪ representative sample is just that…a sample that truly "represents" your target market or audience for your business. nonparametric testing.By now, many of us have seen the recent AT&T commercials featuring a man dressed in a suit asking a group of adorable small children seated around a table "Which is better…bigger or smaller?" Naturally, the children all respond with a resounding, unanimous "bigger!" The suited man then asks "What about a tree house? Would you rather have a bigger tree house or a smaller one?" The children unanimously respond again that they would prefer a larger tree house, primarily because they would like to have a disco and a large, flat screen television. But with huge samples, normality testing will detect tiny deviations from Gaussian, differences small enough so they shouldn't sway the decision about parametric vs. Normality tests ask the question of whether there is evidence that the distribution differs from Gaussian. ![]() But normality tests answer a different question. What you want to know is whether the distribution differs enough from Gaussian to cast doubt on the usefulness of parametric tests. But normality tests don't answer the question you care about. Normality tests work well with large samples, which contain enough data to let you make reliable inferences about the shape of the distribution of the population from which the data were drawn. If you choose a nonparametric test, but actually do have Gaussian data, you haven't lost much as nonparametric tests have nearly as much power as parametric tests when the sample size is large. If you choose a parametric test and your data are not really Gaussian, you haven't lost much as the parametric tests are robust to violation of the Gaussian assumption, especially if the sample sizes are equal (or nearly so). The decision to choose a parametric or nonparametric test matters less with huge samples (say greater than 100 or so). Small samples simply don't contain enough information to let you make reliable inferences about the shape of the distribution in the entire population. ![]() Unfortunately, normality tests have little power to detect whether or not a sample comes from a Gaussian population when the sample is tiny. ![]() If you choose a nonparametric test, but actually do have Gaussian data, you are likely to get a P value that is too large, as nonparametric tests have less power than parametric tests, and the difference is noticeable with tiny samples. Parametric tests are not very robust to deviations from a Gaussian distribution when the samples are tiny. If you choose a parametric test and your data do not come from a Gaussian distribution, the results won't be very meaningful. Your decision to choose a parametric or nonparametric test matters the most when samples are small (say less than a dozen values). ![]()
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