Glossary
Confidence Level
The confidence level is the probability that a confidence interval contains the true parameter value. Common choices are 90%, 95%, and 99%. Higher confidence levels produce wider intervals and require larger sample sizes to achieve the same precision.
Definition
The confidence level is the probability that a confidence interval contains the true parameter value. Common choices are 90%, 95%, and 99%. Higher confidence levels produce wider intervals and require larger sample sizes to achieve the same precision.
Why It Matters
Choosing a confidence level involves a trade-off between certainty and precision. A 99% confidence interval is wider than a 95% interval, providing greater assurance that the true parameter lies within the range but yielding a less informative estimate. In practice, 95% is the most commonly used level, balancing reasonable certainty with practical interval width. Understanding confidence levels is essential for correctly interpreting survey results, clinical trial findings, and policy evaluations.
Example
A polling organisation estimates that 42% of voters support a candidate, with a margin of error of plus or minus 3 percentage points at the 95% confidence level. This means that if the same survey were repeated many times, approximately 95% of the resulting intervals would contain the true proportion of voter support. Raising the confidence level to 99% would widen the margin of error to approximately plus or minus 3.9 percentage points.
Related Terms
Software Notes
- SPSS: Set in Analyze > Descriptive Statistics > Explore or via Options in various test dialogs
- R:
t.test(x, conf.level = 0.95)— changeconf.levelto 0.90 or 0.99 as needed - Stata:
mean varname, level(95)— adjust thelevel()option; also applies tociandregress