Effect of Validity and Reliability in Research.

The precision with which you measure things also has a major impact on sample size: the worse your measurements, the more subjects you need to lift the signal (the effect) out of the noise   (the errors in measurement). Precision is expressed as validity and reliability. Validity represents how  well a variable measures what it is supposed to. Validity is important in descriptive studies: if the validity of the main variables is poor; you may  need thousands rather than hundreds of subjects.

Reliability tells you how reproducible your measures are on a retest, so it impacts experimental studies. The more reliable a measure, the less subjects you need to see, a small change in the measure. For example, a controlled trial with 20 subjects in each group or a crossover with 10 subjects may be sufficient to characterize even a small effect, if the measure is highly reliable.