Hypothesis and Research Design

The hypothesis example above is very general and the testability could be improved by making it more specific, such as Transthoracic bioimpedance is lower when measured by gel electrodes than measured by textile electrodes using a two-electrode setup if this is the relevant setup we want to test. It is easier to test this hypothesis because it implies only one certain type of measurement, and reduces the chance of an inconclusive result. A 

It is a good idea to know how many units (i.e., items or subjects) are needed to test our hypothesis. Unless we test all the units in a population, we are only testing a sample of the whole population. To make a general conclusion about the population, we need to show that the effect that we observed was not likely the result of chance from random variation in our sample. If we choose too few units, we may end up with an inconclusive result and a worthless study, and if we choose too many, we are wasting resources (e.g., sacrificing more animals than needed). Hence, sample size consideration is of ethical relevance (Bacchetti et al., 2005). 

In practice, the sample size calculation is not done by hand, but by computer programs (such as the free G Power ), which lets you choose a statistical test, asks for the necessary inputs (i.e., a, P, variance, and effect size), and gives you the minimum required sample size. They can also be used to determine the power of your test given the sample size, a, S, and effect size. Because of all these unknowns, it is a good idea to consult a biostatistician on these matters if possible. 

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