In this article we will look at these issues. To be fair it is not an intuitive concept but requires some reflection and effort to understand, calculate and interpret correctly. It has now gained wide acceptance although many of us are not quite confident about it ( 4). The concept of the CI was introduced by Jerzy Neyman in a paper published in 1937 ( 3). Thus we can calculate CI of means, medians, proportions, odds ratios (ORs), relative risks, numbers needed to treat, and so on. Wherever sampling is involved, we can calculate CI. They also indicate the precision or reliability of our observations-the narrower the CI of a sample statistic, the more reliable is our estimation of the underlying population parameter. In particular, they often offer a more dependable alternative to conclusions based on the P value ( 2). The CI is a descriptive statistics measure, but we can use it to draw inferences regarding the underlying population ( 1). The CI addresses this issue because it provides a range of values which is likely to contain the population parameter of interest. How well the sample statistic estimates the underlying population value is always an issue. This range is the confidence interval (CI). Practically speaking, it is possible to use a sample statistic and estimates of error in the sample to get a fair idea of the population parameter, not as a single value, but as a range of values. Strictly speaking, without doing a census it is not possible to get true population values. If we have a large enough and adequately representative sample, it is logical to presume that the sample statistics would be close to the ‘true values’, that is the population parameters, but they would probably not be identical to them. However, if we are studying samples, then what we have in our hand at study end are the sample statistics. But how do we do this? If we are doing ‘census’ type of studies, then the measured values are directly the population parameters since a census covers the entire population. Values obtained from samples are referred to as ‘sample statistics’ which we have to use to garner idea of corresponding values in the underlying population, that are referred to as ‘population parameters’. Random sampling also allows methods based on probability theory to be applied to the data.Īlthough we work with samples, our goal is to describe and draw inferences regarding the underlying population. There are various strategies for sampling, but, wherever feasible, random sampling strategies are to be preferred since they ensure that every member of the population has an equal and fair chance of being selected for the study.
#Standard normal table confidence interval trial
The conclusions from these alternative trial designs are based on CI values rather than the P value from intergroup comparison.īiomedical research is seldom done with entire populations but rather with samples drawn from a population. Of late, clinical trials are being designed specifically as superiority, non-inferiority or equivalence studies. Use of the CI supplements the P value by providing an estimate of actual clinical effect. However, statistical significance in terms of P only suggests whether there is any difference in probability terms. Clinical importance is best inferred by looking at the effect size, that is how much is the actual change or difference. Conflict between clinical importance and statistical significance is an important issue in biomedical research. A 99% CI will be wider than 95% CI for the same sample. Although the 95% CI is most often used in biomedical research, a CI can be calculated for any level of confidence. The factors affecting the width of the CI include the desired confidence level, the sample size and the variability in the sample. Calculation of the standard error varies depending on whether the sample statistic of interest is a mean, proportion, odds ratio (OR), and so on. Calculation of the CI of a sample statistic takes the general form: CI = Point estimate ± Margin of error, where the margin of error is given by the product of a critical value (z) derived from the standard normal curve and the standard error of point estimate. This range is the confidence interval (CI) which is estimated on the basis of a desired confidence level. It is possible to use a sample statistic and estimates of error in the sample to get a fair idea of the population parameter, not as a single value, but as a range of values. Although we work with samples, our goal is to describe and draw inferences regarding the underlying population. Biomedical research is seldom done with entire populations but rather with samples drawn from a population.
0 kommentar(er)
