STATISTICAL SIGNIFICANCE AND STATISTICAL POWER IN HYPOTHESIS-TESTING

Experimental design requires estimation of the sample size required to produce a meaningful conclusion. Often, experimental results are performed with sample sizes which are inappropriate to adequately support the conclusions made. In this paper, two factors which are involved in sample size estimation are detailed-namely type I (α) and type II (β) error. Type I error can be considered a 'false positive' result while type II error can be considered a 'false negative' result. Obviously, both types of error should be avoided . The choice of values for α and β is based on an investigator's understanding of the experimental system, not on arbitrary statistical rules. Examples relating to the choice of α and β are presented, along with a series of suggestions for use in experimental design.; Experimental design requires estimation of the sample size required to produce a meaningful conclusion. Often, experimental results are performed with sample sizes which are inappropriate to adequately support the conclusions made. In this paper, two factors which are involved in sample size estimation are detailed-namely type I (α) and type II (β) error. Type I error can be considered a 'false positive' result while type II error can be considered a 'false negative' result. Obviously, both types of error should be avoided. The choice of values for α and β is based on an investigator's understanding of the experimental system, not on arbitrary statistical rules. Examples relating to the choice of α and β are presented, along with a series of suggestions for use in experimental design.