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A paired difference test is designed for situations where there is dependence between pairs of measurements (in which case a test designed for comparing two independent samples would not be appropriate). That applies in a within-subjects study design, i.e., in a study where the same set of subjects undergo both of the conditions being compared.
Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [ 4 ]
Comparative research is a research methodology in the social sciences exemplified in cross-cultural or comparative studies that aims to make comparisons across different countries or cultures. A major problem in comparative research is that the data sets in different countries may define categories differently (for example by using different ...
Scientific experiments often require comparing two (or more) sets of data. In some cases, the data sets are paired, meaning there is an obvious and meaningful one-to-one correspondence between the data in the first set and the data in the second set, compare Blocking (statistics). For example, paired data can arise from measuring a single set ...
Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).
Thus, it is the first step to identifying subsets of a data set conforming to particular causal pathway based on the combinations of covariates prior to quantitative statistical analyses testing conformance to a model; and helps qualitative researchers to correctly limit the scope of claimed findings to the type of observations they analyze.
This is a truly nested variant which contains an outer loop of k sets and an inner loop of l sets. The total data set is split into k sets. One by one, a set is selected as the (outer) test set and the k - 1 other sets are combined into the corresponding outer training set. This is repeated for each of the k sets.
Sample sizes may be evaluated by the quality of the resulting estimates, as follows. It is usually determined on the basis of the cost, time or convenience of data collection and the need for sufficient statistical power. For example, if a proportion is being estimated, one may wish to have the 95% confidence interval be