Understanding Health Research, a tool for making sense of health studies: use of statistics

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• Format Websites
• Language/s English
• Target Audience Further education, Self-directed learning
• EBM Stage 3 - Appraising evidence
• Duration 5-15 mins
• Difficulty Introductory

Key Concepts addressed

• 2-3b Relative measures of effects can be misleading
• 2-3d Fair comparisons with few people or outcome events can be misleading
• 2-3f Confidence intervals should be reported

Details

In health research, researchers typically use statistics to determine two things:

• Could the results have happened by chance? (statistical significance)
• How large was the effect found in the study? (effect size)

Statistical significance

Random chance can affect any study, and any measurement a researcher takes will be affected by some degree of chance. It is very important that researchers are not led by random chance into making false conclusions.

Imagine researchers are comparing two weight loss treatments, A and B. Their measurements suggest that more participants lose weight with Treatment A than with Treatment B. Assuming there is no bias in their study, there are two possible explanations for this:

• The difference measured was due to chance, and Treatment A may not be any more effective than Treatment B
• The difference measured was not due to chance, and Treatment A really is more effective than Treatment B

Researchers use statistical tests to decide how likely it is that results are due to chance. If a result is probably not due to chance, the result is described as statistically significant. If the result is statistically significant, researchers may conclude that Treatment A really is more effective than Treatment B in similar situations to their trial.

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