Not Too Big, Not Too Small

Article

Bertoni, Christopher N. and Bertoni, Bridget   (ASQ)   Acumen LLC, Burlingame, CA

Quality Progress    Vol. 51    No. 12
QICID: 41005    December 2018    pp. 58-64
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Article Abstract

Running significance tests with small sample sizes can produce incomplete or inaccurate results. In order to overcome this problem, the largest sample size available is chosen to ensure a better picture of the data as well as more desirable confidence intervals. The question is, when is a sample size too large and is there an optimal size for significance tests? How can an optimal sample size be found? Knowing what test to use, what the significant results are and what is an acceptable probability of error are good ways to start finding the right sample size.

Keywords

Sample size, Significance tests, Statistics, Data analysis


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