Statistics help turn data into insight—but not all statistics serve the same purpose. In educational research, two major types of statistics are used to understand and interpret data: descriptive and inferential. Knowing the difference—and when to use each—helps researchers, educators, and analysts make informed, evidence-based decisions.
Kent State’s online Master of Education in Research, Measurement, and Statistics equips students to confidently apply both, building the skills needed to move from raw data to real-world conclusions.
What Are Descriptive Statistics?
Descriptive statistics summarize and organize data so that it can be easily understood. They describe what is happening within a specific dataset, without making claims beyond the group studied.
Common descriptive statistics include:
- Mean, median, and mode (measures of central tendency)
- Range and standard deviation (measures of variability)
- Frequency tables and charts
Example:
An education researcher calculates the average test score for students in a math class and displays the score distribution in a bar chart. This helps describe how students performed—but does not explain why or predict outcomes for other students.
When to use descriptive statistics:
- To summarize survey or assessment results
- To explore patterns in existing data
- To communicate findings clearly to stakeholders
What Are Inferential Statistics?
Inferential statistics go a step further by allowing researchers to draw conclusions about a population based on a sample. These methods test hypotheses and examine relationships between variables.
Common inferential techniques include:
- t-tests and ANOVA (compare group differences)
- Correlation and regression (examine relationships)
- Confidence intervals and significance testing
Example:
A researcher uses a t-test to determine whether students who received tutoring scored significantly higher than those who did not. This allows them to make inferences [SJ1] [SJ2] about the effectiveness of the tutoring program.
When to use inferential statistics:
- To test research questions or hypotheses
- To evaluate program effectiveness
- To predict outcomes and inform decisions
How They Work Together
Descriptive and inferential statistics are not opposites—they are complementary tools. Most research studies begin with descriptive analysis to understand the dataset, then apply inferential methods to interpret and generalize findings.
Together, they allow researchers to:
- Identify trends
- Compare groups
- Evaluate interventions
- Support data-driven decisions
How Kent State’s RMS Program Prepares You
Kent State’s online M.Ed. in Research, Measurement, and Statistics offers a strong foundation in both descriptive and inferential statistics through coursework in:
- Statistical methods and data analysis
- Research design and measurement
- Program evaluation
- Advanced quantitative techniques
Students learn to analyze real data sets, apply appropriate statistical tools, and communicate results to academic and professional audiences—skills that are in high demand across education, research, and organizational settings.
About Kent State’s Online M.Ed. in Research, Measurement, and Statistics
Kent State’s online Master of Education in Research, Measurement, and Statistics prepares professionals to design, analyze, and interpret data that informs educational and organizational decision-making. The program combines advanced coursework in statistics, research design, and measurement with practical applications, equipping students with in-demand skills for roles in assessment, program evaluation, institutional research, and data analysis. Designed for working professionals, the flexible online format allows students to build deep analytical expertise while balancing career and life responsibilities.
Ready to Strengthen Your Data Skills?
Kent State’s online RMS program prepares students to turn data into insight using both descriptive and inferential statistics. Whether you’re working in education, research, policy, or program evaluation, this degree helps you make informed decisions backed by evidence.
