How to Read Statistical Results
Learn to interpret p-values, confidence intervals, effect sizes, and the standard tables found in research papers and statistical software output.
You'll learn
How to read and interpret statistical output tables.
Use this when
You receive analysis results and need to understand the numbers.
The p-Value: What It Really Means
The p-value is the probability of observing results at least as extreme as yours, assuming the null hypothesis is true. It is NOT the probability that the null hypothesis is true or false.
- ●p < 0.05 → "statistically significant" by convention (Fisher's threshold, somewhat arbitrary)
- ●p < 0.01 → highly statistically significant
- ●p < 0.001 → very highly statistically significant
- ●p ≥ 0.05 → "not statistically significant" — does NOT mean no effect exists
A p-value tells you NOTHING about the size or practical importance of an effect. A study with 100,000 participants can find p < 0.001 for a completely trivial difference. Always pair p-values with effect sizes.
Confidence Intervals (95% CI)
A 95% confidence interval gives a range of plausible values for the true population parameter. If you repeated the study 100 times, approximately 95 of those intervals would contain the true value.
- ●Narrower CI → more precision (larger sample size)
- ●Wider CI → less precision (smaller sample size or more variability)
- ●If a CI for a difference does not include zero, the result is statistically significant at p < 0.05
- ●If a CI for a ratio (OR, RR, HR) does not include 1.0, it is statistically significant
Example: Mean difference in HbA1c between treatment groups = -0.8% (95% CI: -1.2 to -0.4). This means the treatment reduced HbA1c by 0.8 percentage points, and we are 95% confident the true effect is between -0.4 and -1.2.
Effect Sizes: How Big Is the Effect?
Effect sizes quantify the magnitude of an effect, independent of sample size. Always report them alongside p-values.
- ●Cohen's d (t-tests): Small = 0.2, Medium = 0.5, Large = 0.8
- ●η² (ANOVA): Small = 0.01, Medium = 0.06, Large = 0.14
- ●r (correlation): Small = 0.1, Medium = 0.3, Large = 0.5
- ●OR / RR (binary outcomes): Use 95% CI to communicate magnitude
- ●Cramér's V (chi-square): Small = 0.1, Medium = 0.3, Large = 0.5
Reading Table 1 (Baseline Characteristics)
Every clinical study starts with "Table 1," which compares baseline characteristics between groups. Here is how to read it:
- 1.Each row is a variable; columns are groups (e.g., Control vs. Treatment)
- 2.Continuous variables: reported as Mean ± SD or Median [IQR]
- 3.Categorical variables: reported as n (%) for each category
- 4.The final column shows p-values from comparison tests (t-test, chi-square, etc.)
- 5.If groups differ significantly at baseline (p < 0.05), this is a potential confound — authors should adjust for it in regression models
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