As a domain expert in statistics and sampling theory, I can provide a comprehensive explanation of the concept of confidence level in sampling. Sampling is a fundamental process in statistics, where a subset of individuals from a larger population is studied to make inferences about the entire population. The confidence level is a critical concept in this process, as it quantifies the level of certainty we have about our statistical results. The confidence level is expressed as a percentage and it represents how often the true value of a population parameter (like the mean or proportion) would fall within a calculated range, known as the confidence interval, if we were to repeat the sampling process an infinite number of times. This range is derived from the sample data and is used to estimate the population parameter. When we say we are operating at a 95% confidence level, it means that if we were to take all possible samples of the same size and calculate the confidence interval for each, 95% of those intervals would contain the true population parameter. It does not mean there is a 95% chance that the true value is in the calculated interval for a single sample; rather, it is the proportion of intervals that would contain the true value over all possible samples. Similarly, a 99% confidence level indicates a higher degree of certainty. If we were to construct 99% confidence intervals for all possible samples, 99% of these intervals would include the true population parameter. This level of confidence is often used when the stakes are high, and a higher degree of accuracy is required. The choice of confidence level depends on the context of the study and the acceptable level of risk. Most researchers opt for a 95% confidence level because it provides a good balance between precision and the practicality of obtaining a sufficiently large sample size. However, in some cases, such as legal proceedings or critical medical studies, a 99% confidence level might be more appropriate. It's important to note that the confidence level is not a measure of the quality of the data or the validity of the conclusions. It is a statement about the reliability of the method used to estimate the population parameter. A higher confidence level does not necessarily mean better data; it simply means that the interval is constructed to be wider, capturing a greater range of potential values for the population parameter. In summary, the confidence level is a statistical tool that provides a measure of the reliability of our inferences about a population based on sample data. It is a crucial concept in the interpretation of statistical results and should be carefully considered in the design and analysis of any study. read more >>

It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain; the 99% confidence level means you can be 99% certain. Most researchers use the 95% confidence level.read more >>