What one wants is a good tradeoff between a parsimonious design and one that provides the highest level of confidence. Of course, this depends upon the type of questions asked and relationships predicted. Designing with confidence does not refer to the power of statistical estimates, although there is such a thing as statistical correspondence validity which means that the intended analysis is consistent with the design to be used. Confidence , as the term means here, simply means that the prospective design is one the researcher feels comfortable with and is likely to be appreciated by the rest of the scientific community. This is often referred to as the requirement of replication .
Replication demonstrates, as design issues in general do, the quality of inference.
Nothing is ever demonstrated directly and completely. Only by what seems tedious, rigorous, and systematic does more and more tenable generalizations become possible. Interpreting research is perhaps the prime example of inference. Interpretation is made on the basis of data analysis using some sort of statistic . A statistic is a mathematical tool or formula used to test the probability of being right or wrong when we say a hypothesis is true or false.