Statistical Analysis
Elementary School
Definition
The use of statistics to interpret data, identify patterns, and judge whether results are meaningful rather than due to chance. Scientists rely on statistical analysis to draw reliable conclusions and to decide how much confidence to place in their findings.
Worked examples
A researcher measures plant growth under two fertilizers and finds mean heights of 15.2 cm vs. 14.8 cm, with p = 0.03.
Statistical analysis shows the difference is likely real (p < 0.05), not just random variation.
Comparing 50 test scores, the average is 78 with a standard deviation of 12, revealing most scores cluster near the mean.
Statistics quantify the center and spread, helping identify patterns in the data set.
Common mistakes
- A small p-value (e.g., 0.02) proves the hypothesis is true. → A small p-value means the result is unlikely due to chance, but doesn't prove causation or absolute truth. Statistical significance indicates confidence in a pattern, not certainty about the underlying cause.
- Correlation between ice cream sales and drowning means ice cream causes drowning. → Correlation does not imply causation — both may rise due to summer weather (a confounding variable). Statistical analysis reveals associations; experiments are needed to establish cause-and-effect.
- The mean is always the best summary of data. → Use median for skewed data or outliers; mean works best for symmetric distributions. Different statistics suit different data shapes; choosing the right one improves interpretation.
Where you'll use it next
You'll apply statistical analysis in biology labs to test hypotheses, in chemistry to interpret experimental error, in physics to analyze measurement uncertainty, and across all sciences to design studies and publish findings with confidence intervals.
See also
Reviewed by Pat Cheng, M.Ed. — StudyPug Curriculum Lead · Last updated June 6, 2026