Small Sample Size and Data Quality Issues Illustrated Using Tornado Occurrence Data

Abstract

A major challenge in weather research is associated with the size of the data sample from which evidence can be presented in support of some hypothesis. This issue arises often in severe storm research, since severe storms are rare events, at least in any one place. Although large numbers of severe storm events (such as tornado occurrences) have been recorded, some attempts to reduce the impact of data quality problems within the record of tornado occurrences also can reduce the sample size to the point where it is too small to provide convincing evidence for certain types of conclusions. On the other hand, by carefully considering what sort of hypothesis to evaluate, it is possible to find strong enough signals in the data to test conclusions relatively rigorously. Examples from tornado occurrence data are used to illustrate the challenge posed by the interaction between sample size and data quality, and how it can be overcome by being careful to avoid asking more of the data than what they legitimately can provide. A discussion of what is needed to improve data quality is offered.