What's the difference between descriptive and inferential statistics?

In recent years, the embrace of information technology in the health care field has significantly changed how medical professionals approach data collection and analysis. For nurses who hold a Doctor of Nursing Practice (DNP) degree, many aspects of their work depend on data. This is true whether they fill leadership roles in health care organizations or serve as nurse practitioners.

However, the use of data goes well beyond storing electronic health records (EHRs). Increasingly, insights are driving provider performance, aligning performance with value-based reimbursement models, streamlining health care system operations, and guiding care delivery improvements. As a result, DNP-prepared nurses are now more likely to have some proficiency in statistics and are expected to understand the intersection of statistical analysis and health care.

For nurses to succeed in leveraging these types of insights, it’s crucial to understand the difference between descriptive statistics vs. inferential statistics and how to use both techniques to solve real-world problems. According to the American Nurses Association (ANA), nurses at every level should be able to understand and apply basic statistical analyses related to performance improvement projects.

In Bradley University’s online DNP program, students study the principles and procedures of statistical interpretation. Here’s what nursing professionals need to know about descriptive and inferential statistics, and how these types of statistics are used in health care settings.

What Are Descriptive Statistics?

In essence, descriptive statistics are used to report or describe the features or characteristics of data. They summarize a particular numerical data set,or multiple sets, and deliver quantitative insights about that data through numerical or graphical representation.

Descriptive statistics only reflect the data to which they are applied. A descriptive statistic can be:

• A measure of central tendency, like mean, median, or mode: These are used to identify an average or center point among a data set
• A measure of dispersion or variability, like variance, standard deviation, skewness, or range: These reflect the spread of the data points
• A measure of distribution, like the quantity or percentage of a particular outcome: These express the frequency of that outcome among a data set

Virtually any quantitative data can be analyzed using descriptive statistics, like the results from a clinical trial related to the side effects of a particular medication.

Descriptive statistics expressing a measure of central tendency might show the mean age of people who tried the medication was 37. Additionally, as a measure of distribution, descriptive statistics could show 25% of the group experienced mild side effects, while 2% felt moderate to severe side effects and 73% felt no side effects.

The raw data can be represented as statistics and graphs, using visualizations like pie charts, line graphs, tables, and other representations summarizing the data gathered about a given population.

What Are Inferential Statistics?

Inferential statistics are used to make conclusions, or inferences, based on the available data from a smaller sample population. This is often done by analyzing a random sampling from a much broader data set, like a larger population. Conclusions drawn from this sample are applied across the entire population.

The relevance and quality of the sample population are essential in ensuring the inference made is reliable. This is true whether the population is a group of people, geographic areas, health care facilities, or something else entirely. A representative sample must be large enough to result in statistically significant findings, but not so large it’s impossible to analyze.

Inferential statistics techniques include:

• Hypothesis tests, or tests of significance: These involve confirming whether certain results are significant and not simply by chance
• Correlation analysis: This helps determine the relationship or correlation between variables
• Logistic or linear regression analysis: These methods enable inferring and predicting causality and other relationships between variables
• Confidence intervals: These help identify the probability an estimated outcome will occur

As an example, inferential statistics may be used in research about instances of comorbidities. Instead of canvassing vast health care records in their entirety, 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 from which the sample was taken.

Studying a random sample of patients within this population can reveal correlations, probabilities, and other relationships present in the patient data. These findings may help inform provider initiatives or policymaking to improve care for patients across the broader population.

What’s the Difference Between Descriptive Statistics vs. Inferential Statistics?

The key difference between descriptive and inferential statistics is descriptive statistics aren’t used to make an inference about a broader population, whereas inferential statistics are used for this purpose. Rather than being used to report on the data set itself, inferential statistics are used to generate insights across vast data sets that would be difficult or impossible to analyze.

Essentially, descriptive statistics state facts and proven outcomes from a population, whereas inferential statistics analyze samplings to make predictions about larger populations.

In the example of a clinical drug trial, the percentage breakdown of side effect frequency and the mean age represents statistical measures of central tendency and normal distribution within that data set.

However, inferential statistics methods could be applied to draw conclusions about how such side effects occur among patients taking this medication. The resulting inferential statistics can help doctors and patients understand the likelihood of experiencing a negative side effect, based on how many members of the sample population experienced it.

Since descriptive statistics focus on the characteristics of a data set, the certainty level is very high. Outliers and other factors may be excluded from the overall findings to ensure greater accuracy, but calculations are often much less complex and can result in solid conclusions.

However, inferential statistics are designed to test for a dependent variable — namely, the population parameter or outcome being studied — and may involve several variables. The calculations are more advanced, but the results are less certain. There will be a margin of error as well. After all, inferential statistics are more like highly educated guesses than assertions. A sampling error may skew the findings, although a variety of statistical methods can be applied to minimize problematic results.

When Should You Use Descriptive Statistics vs. Inferential Statistics in Nursing?

The type of statistical analysis used for a study — descriptive, inferential, or both — will depend on the hypotheses and desired outcomes. To carry out evidence-based practice, advanced nursing professionals who hold a Doctor of Nursing Practice can expect to run quick mental math or conduct an in-depth statistical test in a variety of 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 can also come into play for professionals like family nurse practitioners or emergency room nurse managers who must know how to calculate variance in a patient’s blood pressure or blood sugar. Moreover, in a family clinic, nurses might analyze the body mass index (BMI) of patients at any age.

But descriptive statistics only make up part of the picture, according to the journal American Nurse. 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. 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.

Following up with inferential statistics can be an important step toward improving care delivery, safety, and patient experiences across wider populations. Since it’s virtually impossible to survey all patients who share certain characteristics, Inferential statistics are crucial in forming predictions or theories about a larger group of patients. The sample data can indicate broader trends across the entire population.

Such statistics have clear use regarding the rise of population health. For instance, examining the health outcomes and other data of patient populations like minority groups, rural patients, or seniors can help nurse practitioners develop better initiatives to improve care delivery, patient safety, and other facets of the patient experience. Looking at how a sample set of rural patients responded to telehealth-based care may indicate it’s worth investing in such technology to increase telehealth service access. Techniques like hypothesis testing and confidence intervals can reveal whether certain inferences will hold up when applied across a larger population.

How Can You Learn About Statistics for Nursing?

Aspiring leaders in the nursing profession must be confident in using statistical analysis to inform empirical research and therefore guide the creation and application of evidence-based practice methods. At Bradley University, the online Doctor of Nursing Practice program prepares students to leverage these techniques in health care settings. This is true of both DNP tracks at Bradley, namely:

• The DNP-FNP track, for those who enroll with a Bachelor of Science in Nursing (BSN) and aspire to become family nurse practitioners (FNPs)
• The DNP-Leadership track, for those who enroll with a Master of Science in Nursing (MSN) and aspire to health care leadership roles

The curricula of both the DNP-FNP and DNP-Leadership programs include courses intended to impart key statistical knowledge and data analysis skills to be used in a nursing career, such as:

• Research Design and Statistical Methods
• Methods in Evidence Based Practice
• Advanced Health Informatics

Research Design and Statistical Methods introduces an examination of research study design/methodology, application, and interpretation of descriptive and inferential statistical methods appropriate for critical appraisal of evidence. Use of analytic software for data management and preliminary analysis prepares students to assess quantitative and qualitative data, understand research methodology, and critically evaluate research findings.

Methods in Evidence Based Practice introduces students to theories related to Research Utilization (RU) and Evidence-based Practice (EBP) and provides opportunities to explore issues and refine questions related to quality and cost-effective healthcare delivery for the best client outcomes. Methods to collect evidence, plan changes for the transformation of practice, and evaluate quality improvement methods will be discussed.

Finally, the Advanced Health Informatics course examines the current trends in health informatics and data analytic methods. It provides opportunities for the advanced practice nurse (APN) to apply theoretical concepts of informatics to individual and aggregate level health information. Emphasis is placed on the APN’s leadership role in the use of health information to improve health care delivery and outcomes.

Gain Statistical Expertise in Bradley’s Online DNP Program

Bradley’s online DNP program offers nursing students a flexible learning environment that can work around their existing personal and professional needs.

The DNP-FNP track is offered 100% online with no campus residency requirements. It involves completing 10 semesters and 1,000 clinical hours, which takes full-time students approximately 3.3 years to complete.

The DNP-Leadership track is also offered 100% online, without any campus residency requirements. This program involves finishing eight semesters and 1,000 clinical hours, taking students 2-2.7 years to complete if they study full time.

Interested in learning more about where an online DNP could take your nursing career? Visit our online DNP program page and contact an enrollment advisor today for more information.

Recommended Readings:

Online Doctor of Nursing Practice

Principles of Nursing Leadership: Jobs and Trends

Career Profile: Nursing Professor Salaries, Skills, and Responsibilities

Sources:

American Nurse — Research 101: Descriptive Statistics

Indeed — Descriptive vs Inferential Statistics

ThoughtCo — The Difference Between Descriptive and Inferential Statistics

Investopedia — Descriptive Statistics

My Market Research Methods — Descriptive vs Inferential Statistics: What’s the Difference?

Researchgate — Interpretation and Use of Statistics in Nursing Research