Interpreting confidence levels and confidence intervals
What are confidence intervals?
By Saul McLeod , published June 10, As the sample size increases, the range of interval values will narrow, meaning that you know that mean with much more accuracy compared with a smaller sample. We can visualize this using a normal distribution see the below graph. For example, the probability of the population mean value being between It is more or less impossible to study every single person in a population so researchers select a sample or sub-group of the population. This means that the researcher can only estimate the parameters i.
A confidence interval, in statistics, refers to the probability that a population parameter will fall between two set values for a certain proportion of times. Confidence intervals measure the degree of uncertainty or certainty in a sampling method. Confidence interval and confidence level are interrelated but are not exactly the same. Statisticians use confidence intervals to measure uncertainty. For example, a researcher selects different samples randomly from the same population and computes a confidence interval for each sample.
Statisticians use a confidence interval to describe the amount of uncertainty associated with a sample estimate of a population parameter. How would you interpret this statement? This is incorrect. Like any population parameter , the population mean is a constant, not a random variable. It does not change.