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. There are about 100 common statistical tests. A test of one's hypothesis can always reach statistical significance by increasing the sample size, and that's just because the way cutoffs are placed in the tables of numbers called significance tables. However, there's a difference between statistical and meaningful significance. Statistical significance is no guarantee of meaningful, or social or psychological significance.