Confidence level

A measurement of the frequency of confidence intervals, the confidence level refers to the number of samples within a population that include a true population parameter, as different samples selected for surveys can often carry a variety of confidence intervals. Therefore, the percentage of confidence intervals taken across a number of tests containing the true value of the parameter should match the confidence level which provides a level of probability (i.e. a 99% confidence level should result in 99% of intervals matching the true population parameters).

The size of a confidence interval may be influenced by a number of factors including population variability or population size. It is generally witnessed that the greater the sample size, the better the estimate for achieving population parameters.

For example, if an exit poll for a political referendum were to predict a victory of 64% for the ‘Yes’ vote and applied a 90% confidence level, this would suggest that the results may fluctuate by 6.4% either way, giving the ‘Yes’ vote a 70.4% win in the best case or a 57.6% win as the worst case. Confidence levels are therefore helpful in that they provide a range of possibility for results, aiding the interpretation of data.

Whilst confidence levels do vary, it is general practice to assume a level of 95% meaning that 95% of the time, confidence intervals should contain the true value of the variable of interest.

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