Loading...
Loading...
Calculate confidence intervals for population mean with precision
The concept was developed by Jerzy Neyman in 1937, revolutionizing statistical inference and hypothesis testing.
The 95% confidence level became standard because it balances precision with practical utility in most research fields.
Clinical trials rely on confidence intervals to determine if new treatments are significantly better than placebos.
Election polls use confidence intervals to report margin of error, typically around 3-4% for national surveys.
Manufacturing uses confidence intervals to ensure product specifications fall within acceptable tolerance ranges.
Investment firms use confidence intervals to estimate expected returns and assess portfolio risk levels.
A 95% CI doesn't mean 95% chance the true value is inside. It means 95% of such intervals would contain it.
Narrower intervals indicate more precise estimates. Larger samples and lower variability produce narrower intervals.
A confidence interval gives a range of plausible values for a population parameter based on sample data. The formula is: CI = Mean +/- (Z-score x Standard Error), where Standard Error = Standard Deviation / sqrt(n).