What’s the difference between descriptive and inferential statistics?

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Group of nurses talking together.

The spread of information technology has significantly changed how all medical professionals approach data collection and analysis. Nurses who’ve earned a Doctor of Nursing Practice are no different: Many aspects of their jobs, whether as managers or independent practitioners, depend on data.

The subject of data goes well beyond electronic health systems. DNP-prepared nurses are now more likely to have some proficiency in statistics, or at least a familiarity with the intersection of number-crunching and health care. While that relationship may seem opaque and full of buzzwords, data is increasingly being used to drive provider performance, align with value-based reimbursement models, streamline health care system operations, and develop and measure care delivery improvements.

For nurses to succeed in leveraging data insights, it’s crucial to understand the difference between descriptive and inferential statistics. And in Bradley’s online DNP program, students will study the principles and procedures for statistical interpretation of data. To start, here’s what to know about descriptive and inferential statistics, and how they’re used in health care.

What are descriptive statistics?

Descriptive statistics are perhaps what most people would recognize, even if they’re not familiar with the term. In essence, descriptive statistics describe the data. They summarize a particular data set, or multiple sets, and deliver quantitative insights through numerical or graphical representation. The mean, median and mode are all descriptive statistics, as are the standard deviation or grade point average — these are all measures of tendency or variance.

The thing to keep in mind is that descriptive statistics are just that: They describe the data to which they are applied and are used to generate insight across vast sets that would be difficult to work through by hand. Regarding professional use cases, descriptive statistics are relied on in many settings and industries. Any data can be analyzed using descriptive statistics, like patient data and outcomes from a pilot program related to medication safety.

What are inferential statistics?

Inferential statistics allow you to use data to make predictions (or inferences) based upon the data. This is in clear contrast to descriptive statistics. Rather than being used to describe the data itself, inferential metrics are used to reveal correlation, proportion or other relationships present in the data.

This is often done by analyzing a sampling from a larger data set. Conclusions that can be drawn from this sample size may then be applied across the entire data. For instance, inferential statistics may be used in research about instances of commorbidities. Instead of canvassing vast records entirely, researchers can analyze a sample set of patients with shared attributes — like those with more than two chronic conditions — and extrapolate results across the larger population with such qualities. This could help inform provider initiatives or policy-making to improve care.

Nurse practitioner working on tablet.How are statistics used in nursing?

There are a wide variety of situations in which both descriptive and inferential statistics are used. Nurses who’ve earned a DNP can expect to encounter data and statistics in several on-the-job situations. For example, nurse executives who oversee budgeting and other financial responsibilities will likely need familiarity with descriptive statistics and their use in accounting. Descriptive statistics will likely also come into play for professionals like family nurse practitioners or emergency room nurse managers. Calculating variance in blood pressure or blood sugar is one example; body mass index analysis in children seen by a family clinic is another.

Inferential statistics are crucial in forming predictions or theories about a population. The sample data can indicate broader trends across the entire set, and such statistics have clear use to today’s nurse regarding the rise of population health. Examining the health outcomes and other data of populations like minorities, rural patients or seniors can help nurse practitioners better develop initiatives that improve care delivery or patient safety. For instance, looking at how rural patients respond to telehealth-based care may indicate to a nurse director that the provider should invest in such technology to increase service and enhance the patient experience.

What can students learn in Bradley’s online DNP program?

The curriculum imparts key statistics knowledge and skills to be used in a nursing career. For instance, ENC 510: Statistical Procedures, will introduce learners to statistical principles and procedures, while NUR 720: Evidence-Based Practice will show them how to collect and analyze data as part of quality improvement or care delivery evaluation. CIS 576: Data Management further allows students to explore data practices and refine their analytical competency. This course will teach students about database query language, data governance, and other issues related to quality, standards, security and privacy.

The Bradley online DNP program offers nurses a flexible learning environment that can work around their existing personal and professional needs. Interested in learning more about the degree? Contact an enrollment advisor today.

Recommended reading: 

How does health informatics impact nurse practitioners?

What are the differences between a DNP and an MD?


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