History of confidence intervals

Unlike hypothesis tests whose origins can be traced to 1279 (25), confidence intervals are a recent development: Jerzy Neyman derived them in the 1930s (20–22). There would be a 50-year lag before medical journals advocated the use of confidence intervals (4, 5, 17, 18).

1. What is the real concept behind confidence intervals?
2. How confidence intervals are created?
3. Why do we construct confidence intervals?
4. What does the 95% confidence interval tell us?

What is the real concept behind confidence intervals?

A confidence interval displays the probability that a parameter will fall between a pair of values around the mean. Confidence intervals measure the degree of uncertainty or certainty in a sampling method. They are most often constructed using confidence levels of 95% or 99%.

How confidence intervals are created?

Confidence intervals are constructed at a confidence level, such as 95 %, selected by the user. ... It means that if the same population is sampled on numerous occasions and interval estimates are made on each occasion, the resulting intervals would bracket the true population parameter in approximately 95 % of the cases.

Why do we construct confidence intervals?

Confidence intervals show us the likely range of values of our population mean. When we calculate the mean we just have one estimate of our metric; confidence intervals give us richer data and show the likely values of the true population mean. ... When it comes to confidence intervals, the smaller the better!

What does the 95% confidence interval tell us?

The 95% confidence interval is a range of values that you can be 95% confident contains the true mean of the population. ... For example, the probability of the population mean value being between -1.96 and +1.96 standard deviations (z-scores) from the sample mean is 95%.