4 Conclusion

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All in all, this paper has shown the application of three methods used for handling small sample sizes in order to practically illustrate their advantages and disadvantages as described in the chapter by Mannila, Nevalainen and Raumolin-Brunberg (2013).

In brief, the advantage of all of them is the simplicity of their application. For pooling and calculating the average of averages, only a calculator or a spreadsheet application like Microsoft Excel are needed. Bootstrapping is slightly more complicated; however, using Excel the process can be simplified compared to using complex statistic software where much prior information and practice are required in order to use the desired methods and functions.

A disadvantage of pooling can be the unbalanced contribution of informants, which should be solved by either applying the average of averages or introducing a fixed quota. The disadvantage of both pooling and the average of averages is that they do not provide confidence intervals. This problem can in turn be solved by bootstrapping in order to show the deviation of all frequencies and the uncertainty of frequency results. Finally, of course the usefulness of the methods mentioned depends on the respective study or project, but they have proven very helpful for handling small sample sizes. However, this might only be true if analyzing all informants without dividing them into subgroups: Studying variation, for example, among female vs. male informants, especially in historical sociolinguistics, one might face the question if each subgroup provides enough informants in order to get accurate results. Logically, the larger a sample the more accurate is the result. But as the respective frequencies of subgroups can be clearly different, it must be kept in mind that smaller samples might not be as accurate and thus it needs to be checked for the respective study whether this could be a problem (Mannila, Nevalainen and Raumolin-Brunberg 2013: 350).

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