For example, if you have a data set with a diastolic blood pressure range of 230 (highest diastolic value) to 25 (lowest diastolic value) = 205 (range), an error probably exists in your data because the values of 230 and 25 aren’t valid blood pressure measures in most studies. The range not only sets boundaries for your data set and indicates the spread, but it also can identify errors in the data. Although the central tendency of data is vital, the range of values (the difference between the maximum and minimum values in the data) also may be important to note.
Measures of variability or dispersion are less common descriptive statistics, but they’re still important because they describe the spread of values across a data set. The mode is the only measure of central tendency that can be analyzed with qualitative/categorical data. The mode can be calculated with data that are quantitative/continuous or qualitative/categorical (have a finite number of categories or groups, such as sex, race, or education level). Some data sets have more than one mode, making them bimodal (two modes) or multimodal (more than two modes). The mode represents the most frequently occurring number or item in a data set. Depending on the number of outliers, they’re either statistically transformed (using a complex statistical formula to balance all variable values) or excluded from the data set. In addition to visually perusing data for outliers, you can identify them using graphical display and complex modeling. These data points are distant from the majority of observations and may be the result of measurement error, coding error, or extreme variability in an observation. When analyzing descriptive statistics, watch for outliers. (See What to do with outliers.) To calculate a mean or median, data must be quantitative/continuous (have an infinite number of possibilities). It’s typically used to describe a data set that has extreme outliers (very low or very high numbers, distant from the majority of data points), in which case the mean will not accurately represent the data. If there are two numbers at the middle of the data set (which occurs when there is an even number of data points), these two numbers are averaged to identify the median. The median is a number found at the exact middle of a set of data. It’s calculated by adding the sum of values in the data and dividing by the total number of observations. The most familiar of these is the mean, or average, which most people use and understand. These measures describe the central portion of frequency distribution for a data set. The most common types of descriptive statistics are the measures of central tendency (mean, median, and mode) that are used in most levels of math, research, evidence-based practice, and quality improvement. Instead, they’re used as preliminary data, which can provide the foundation for future research by defining initial problems or identifying essential analyses in more complex investigations. Sometimes, descriptive statistics are the only analyses completed in a research or evidence-based practice study however, they don’t typically help us reach conclusions about hypotheses. The summaries typically involve quantitative data and visuals such as graphs and charts. They help us understand and describe the aspects of a specific set of data by providing brief observations and summaries about the sample, which can help identify patterns. What are descriptive statistics?ĭescriptive statistics are just what they sound like-analyses that summarize, describe, and allow for the presentation of data in ways that make them easier to understand. And although some statistical analysis is pretty complicated, you don’t need a doctoral degree to understand and use descriptive statistics. How many times have you said (or heard), “Statistics are too complicated”? A significant percentage of graduate students and nurses in clinical practice report feeling anxious when working with statistics. Measures of central tendency (such as mean) and variability (such as standard deviation) are fairly common and easy to use.Nurses at every level should be able to understand and apply basic statistical analyses related to performance improvement projects.Use these tools to analyze data vital to practice-improvement projects.īy Brian Conner, PhD, RN, CNE and Emily Johnson, PhD