Descriptive statistics are a vital component to any research article but may be overlooked to some degree by readers and authors. Adequate descriptive statistics are necessary to provide a full understanding of the characteristics of the sample. Clinician readers need this information so they can appropriately interpret the results and determine if their patient is like the participants in the study. This information is used in the clinical decision-making process when deciding whether the findings of the study can be applied to their patient. Researchers need to examine the findings from the descriptive analysis as a first step to determine the correct type of inferential statistics to use. Descriptive statistics can be used by other researchers to determine the appropriate sample size for future studies.

In most research studies, it is not possible to report the data on all the participants. Descriptive statistics describe and summarize the data by providing an overview of the important characteristics of the sample. The following descriptive statistics are commonly used to describe different characteristics of the sample: frequency distributions, measures of central tendency, and measures of variability. The specific type of descriptive statistic used will depend on the type of variable. Frequency distributions organize and present information on the frequency counts. This can be done within the text, table format, or in a figure. For example, in this issue, Pedulla and colleagues present the relative frequency (%) of participants' level of disability for different demographic and clinical characteristics in table 1. By reviewing this table, the reader can quickly gain insight into the demographics of the relatively large sample. A histogram or bar chart can be used to visualize frequency data. Pedulla and colleagues do this in figure 2, where they present the % of participants that reported meeting different intensities of exercise before and during COVID restrictions.

Measures of central tendency (mean, median, mode) are the most common method of summarizing the characteristics of the participants in a study. Measures of variability (range, minimal and maximum values, interquartile range, and standard deviation) are reported with measures of central tendency. When reported together, the reader can gain insight into the distribution of the characteristic of interest in the sample. This assists in determining if their patient is similar to the participants in the study. For example, in this issue, Fino and colleagues present mean and standard deviation and median and range values for important characteristics of their sample in table 1. The median Dizziness Handicap Inventory score was 14, with the lowest score a 0 and the highest score a 58. This information allows the reader to determine if their patient is similar to the participants in the study. A box and whisker plot is a useful way to visualize the central tendency and variability in data.

Descriptive statistics are an essential component of the results and should be examined carefully by the reader and presented in a logical and comprehensive manner by the authors. They are provided in the text, tables, and figures. Tables and figures are most useful when there is a large sample size and when there is a variety of important demographic and clinical characteristics that need to be summarized. Descriptive statistics provide the necessary information for the reader to interpret the findings and determine if they can be applied to their patient a critical component of evidence-based practice.