Data Analysis
Interpret data and make predictions
Start with the basics and progress through 2 lessons. Each lesson builds on the previous one.
0 of 2 lessons done
Test Your Knowledge
10 questions, new mix each time (from 32)
In This Topic (2 lessons)
Collecting Data
Learn the different methods of collecting data and how to choose the right approach for your research question.
Interpreting Data and Making Predictions
Learn how to analyze data sets, identify patterns and trends, and use data to make reasonable predictions about future outcomes.
Data analysis transforms raw numbers into meaningful insights. By organizing data, calculating statistics, and creating visualizations, we can identify patterns, make comparisons, and draw conclusions. This process is essential in science, business, healthcare, sports, and virtually every field that makes decisions based on information.
The data analysis process includes collecting data, organizing it into useful formats, calculating measures of center (mean, median, mode) and spread (range, standard deviation), creating appropriate graphs, and interpreting results. These skills help you evaluate claims, understand research, and make evidence-based decisions in your own life.
What You'll Learn
- Organize data into tables and frequency distributions
- Calculate mean, median, and mode
- Interpret data displays and identify trends
- Compare datasets using statistical measures
- Draw valid conclusions from data analysis
Frequently Asked Questions
When should I use mean vs. median?
Use mean for symmetric data without outliers. Use median when data has outliers or is skewed, as median is not affected by extreme values. For example, median is better for house prices or salaries.
What makes a graph misleading?
Starting axes at non-zero values, using inconsistent scales, cherry-picking data ranges, or using inappropriate graph types can all make data appear different than it actually is.
How large should a sample be?
Larger samples are generally more reliable, but the needed size depends on population variability and desired precision. For many purposes, samples of 30 or more can provide good estimates.