In most research conducted on groups of people, you will use both descriptive and inferential statistics to analyze your results and draw conclusions. **Descriptive statistics** is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data (Laerd statistics, 2019). Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analyzed or reach conclusions regarding any hypotheses we might have made. Descriptive statistics are very important because if we simply presented our raw data it would be hard to visualize what the data was showing, especially if there was a lot of it. It therefore, enables us to present the data in a more meaningful way, which allows simpler interpretation of the data. They are applied to populations, and the properties of populations, like the mean or standard deviation, are called parameters as they represent the whole population. These measures are important and useful because they allow scientists to see patterns among data, and thus to make sense of that data.

**Inferential statistics** are produced through complex mathematical calculations that allow scientists to infer trends about a larger population based on a study of a sample taken from it (Laerd statistics, 2019). Researchers use inferential statistics to examine the relationships between variables within a sample and then make generalizations or predictions about how those variables will relate to a larger population. It is usually impossible to examine each member of the population individually. So, scientists choose a representative subset of the population, called a statistical sample, and from this analysis, they are able to say something about the population from which the sample came. Nurses, as the largest group of healthcare professionals, are key to quality and safety and to ensuring the best patient outcomes. To make informed practice decisions, nurses need access to aggregate data about their patients and the impact of their care, and they need to know how to interpret that data. The role data plays in quality and safety and the synergistic relationship between data and nursing practice. There are two types of significance used to interpret research studies; Statistical significance and clinical significance. They are not the same thing. One answers the question, Are the statistical results due to random chance? and the other answers the question, So what? Will the results matter to our patients?

In clinical practice, the “clinical significance” of a result is dependent on its implications on existing practice-treatment effect size being one of the most important factors that drive treatment decisions. LeFort (1993) suggests that the clinical significance should reflect “the extent of change, whether the change makes a real difference to subject lives, how long the effects last, consumer acceptability, cost-effectiveness, and ease of implementation”. While there are established, traditionally accepted values for statistical significance testing, this is lacking for evaluating clinical significance. More often than not, it is the judgment of the clinician (and the patient) which decides whether a result is clinically significant or not. Statistical significance is heavily dependent on the study’s sample size; with large sample sizes, even small treatment effects (which are clinically inconsequential) can appear statistically significant; therefore, the reader has to interpret carefully whether this “significance” is clinically meaningful.

A study conducted by (Moore, 2007) compared overall survival in 569 patients with advanced pancreatic cancer who were randomized to receive erlotinib plus gemcitabine versus gemcitabine alone. Median survival was found to be “significantly” prolonged in the erlotinib/gemcitabine arm (6.24 months vs. 5.91 months, P = 0.038). The P = 0.038 means that there is only a 3.8% chance that this observed difference between the groups occurred by chance (which is less than the traditional cut-off of 5%) and therefore, statistically significant. In this example, the clinical relevance of this “positive” study is the “treatment effect” or difference in median survival between 6.24 and 5.91 months – a mere 10 days, which most oncologists would agree is a clinically irrelevant “improvement” in outcomes, especially when considering the added toxicity and costs involved with the combination.

Reference

LeFort, S. M. (1993). The statistical versus clinical significance debate. Image: the journal of nursing scholarship, 25(1), 57-62.

Moore, M. J., Goldstein, D., Hamm, J., Figer, A., Hecht, J. R., Gallinger, S., … & Campos, D. (2007). Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. Journal of clinical oncology, 25(15), 1960-1966.

Houser, J. (2018). Nursing research: Reading, using, and creating evidence (4th ed.). Burlington, MA: Jones & Bartlett Learning.

Laerd statistics, (2019). Descriptive and Inferential Statistics. Retrieved from https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php (Links to an external site.)