Descriptive vs. Inferential Statistics and What They Mean for Students in Clinical Research Training

clinical research diploma

Over the past decade Canada has made significant investments into clinical trial health research through educational institutions, research centres, and hospitals. These investments are intended to maintain Canada’s status as a top site for global pharmaceutical companies to do clinical trial research.

It is therefore important for anyone interested in clinical research to understand the roles of different branches of statistics in this area of study. From research and development to post-marketing monitoring of pharmaceuticals, clinical research relies on statistics. To test hypotheses, answer questions, and adhere to regulations, a study or trial must be designed to gather the appropriate data. Keep reading to gain an understanding of the different methods and purposes of descriptive and inferential statistics.

Tools and Purposes

When you complete a clinical research diploma with the skills to design and write study protocols, you will understand that research relies as much on forming the right questions as it does on answering them. How are description and inferential statistics different?


  • descriptive statistics seeks to make specific observations about a specific group
  • inferential statistics refers to the use of representative data to infer generalizations or conclusions about a general group or population

  • descriptive statistics uses central tendency, dispersion, and skewness
  • inferential statistics uses random sampling, hypothesis tests, confidence regression analysis, and other methods

Statistics involves a lot of working with spreadsheets to help translate data into findings

Statistics involves a lot of working with spreadsheets to help translate data into findings

Where inferential statistics allows researchers to answer more complicated questions about a larger population from a representative sample, the tools of descriptive statistics limit the method to making observations only about the data set itself. Both are important!

The Phases of Clinical Trials and Statistics

Generally, in Ontario, there are four phases to clinical trials—all of which may make use of the methods involved in descriptive and inferential statistics. As an example, consider phase 1, which some researchers also refer to as first in human, depending on the type of research.

  • Aim: to find out about safety, side effects, and dosage of a pharmaceutical product
  • Scope: twenty to eighty people

Most phases of clinical research rely on statistics

Most phases of clinical research rely on statistics

Descriptive statistics would allow researchers to understand how the pharmaceutical affected the tested group. For example, what was the average number of side effects experienced by the group?

Inferential statistics, on the other hand, would allow researchers to compare the results of a test group with that of a control group, or to show the relationships between variables in the data.

The Role of Statistics in Clinical Research Training

Currently, there are more than 3,500 active clinical trials in Ontario, meaning that the province ranks seventh in size for North American clinical trial jurisdictions. To maintain a rigorous research environment similar to the rigor of your clinical research training, statistical analysis must be done.

What are some example questions descriptive and inferential statistical methods seek to answer?


  • How will the data be organized?
  • What data points about the pharmaceutical in question will be represented?

  • Is one treatment or pharmaceutical better than another comparable one?
  • How is the representative population defined?

When you form research questions, you have to determine how statistics will help answer them

When you form research questions, you have to determine how statistics will help answer them

Statistical analysis, whether it is descriptive or inferential, means answering a number of questions to ensure the design of a study minimizes researcher bias, prevents errors, and optimizes data collection as well as the communication of research results.

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