Sampling in Research Series 2: Basic Concepts in Estimating Sample Size

Sampling process/ method has been like an examination to theresearchers’ botheration from generation to generation from thetime of inception towards drawing a representative group of units orcases from a particular population. In series 1 the most popular andfrequently used sampling based on probability theory had been depicted.In this series, sampling for special types of studies will be discussed.It has already been emphatically stated earlier ‘no thumb rule of 100’(or any other fixed number) of samples are permitted for ideal studyor research. Most research publications and MD theses are being seento have been accepted in spite of a wrong sample size. Even manyjournals today publish papers without proper sample size, although itis the responsibility of the reviewer who does not know statistics orepidemiology; the journal concerned is also equally responsible for acherry-picking reviewer with partial knowledge. In fact, such journalsshould be stopped publishing or be blacklisted since they dispense thewrong knowledge to the researchers and the young scientist mightlearn misguidedly. In most of the population studies, 4pq / L2 wherep=Prevalence; q=100 or 1 - Prevalence; and L = permissible limit of errorhas to be <10 % of the prevalence. L or permissible error should notbe taken as absolute term. It’s easy to understand that if the error is10% admitted before a study then the study stands to be null and void.There could be more than 100 types of errors while implementing thestudy like human error, equipment error, interviewer error, respondenterror and so on. If sampling itself has a huge error of 10%; the studywill become a Pandora’s Box of errors only. Thus, it was imperativeto highlight the value of sample size calculation based on probabilitytheory to determine the result of chance and types of different studies.