Small size sampling.

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dc.contributor.authorPathak, Rakesh R
dc.date.accessioned2014-07-30T07:40:14Z
dc.date.available2014-07-30T07:40:14Z
dc.date.issued2012-07
dc.description.abstractBased on the law of large numbers which is derived from probability theory, we tend to increase the sample size to the maximum. Central limit theorem is another inference from the same probability theory which approves largest possible number as sample size for better validity of measuring central tendencies like mean and median. Sometimes increase in sample-size turns only into negligible betterment or there is no increase at all in statistical relevance due to strong dependence or systematic error. If we can afford a little larger sample, statistically power of 0.90 being taken as acceptable with medium Cohen’s d (<0.5) and for that we can take a sample size of 175 very safely and considering problem of attrition 200 samples would suffice.en_US
dc.identifier.citationPathak Rakesh R. Small size sampling. International Journal of Basic & Clinical Pharmacology. 2012 Jul-Aug; 1(1): 43-44.en_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/153382
dc.language.isoenen_US
dc.source.urihttps://www.ijbcp.com/?mno=27649en_US
dc.subjectSystematic errorsen_US
dc.subjectDistributionen_US
dc.subjectSample sizeen_US
dc.subjectProbabilityen_US
dc.titleSmall size sampling.en_US
dc.typeArticleen_US
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