Sample size and power, and careful attention to control groups and confounding factors, are all critical elements of statistical analyses of basic science studies, according to a research paper led by Boston University School of Public Health (BUSPH) members.
Writing in the Journal of the American Heart Association, Dr. Lisa Sullivan and Dr. Janice Weinberg, professors of biostatistics at BUSPH, and colleagues identified a number of common “statistical pitfalls” in basic science research. Among them are study design flaws, such as sample sizes that are too small to “robustly detect or exclude meaningful effects,” and a lack of attention to control groups, which can lead to bias and confounding.
The authors caution that researchers should not cast a statistical net too widely, but instead should conduct analyses only on factors of scientific interest. “Each time a statistical test is performed, it is possible that the statistical test will be significant by chance alone, when, in fact, there is no effect,” they write. “Because each test carries (some) probability of incorrectly claiming significance (i.e., a finite false-positive rate), performing more tests only increases this potential error.”
To read more about the study, go to: http://www.bu.edu/sph/2016/10/27/common-statistical-pitfalls-found-in-basic-science-research/