Introduction to Statistical Hypotheses:
Whenever we want to apply some statistical test to evaluate experimental data, we need to frame our question in an statistical appropriate form. In other words, we need to state a hypothesis from which conclusions can be drawn. Some common questions have already been outlined; in this section, we will see how to formulate these into hypotheses that can then be subjected to statistical evaluation.
Suppose, for example, that we have two sets of replicate data obtained for the same sample. This could be as a result of an analyst repeating the determination on different occasions, or having two different analysts perform the same determination on the same sample. We might want to know several things about the two sets of data:
- Did the two sets of measurements yield the same result?
- Is one set of measurements more or less precise than the other?
- Is one set of results more or less accurate than the other?
Remember that any set of measurements represents a sample from the population of all possible results; there will always be some inherent variation in the mean and standard deviation for each set of replicate measurements. What we therefore need to establish is whether or not our two sets of measurements are drawn from the same, or different populations. In statistical terms, we might therefore propose a hypothesis statement (H) that:
H: “two sets of data (1 and 2) with sample means m1 and m2, are both part of the same population such that their population means μ1 and μ2 are equal (μ1 = μ2)”