In statistics, for any statistical measure, a confidence interval presents a range of possible values within which, with some certainty, we can find the statistical measure of the population. As such, confidence interval is a realistic estimate of (in)precision and sample size of certain research (3). Only the studies with a large sample will give a very narrow confidence interval, which points to high estimate accuracy with a high confidence level. First we have to determine the confidence level for estimating the mean of a parameter in a population. The lower margin of a confidence interval is calculated by deducting the previous formula result from the mean. In our sample example, with the mean cholesterol concentration in the population, the confidence interval would be calculated by using the Z value because of the large size of the sample (N=121) that is normally distributed. Due to the fact that today there are a lot of statistical softwares that calculate and provide confidence intervals for the majority of statistical indicators, we shall rarely calculate a confidence interval manually. Confidence interval can be calculated for difference or ratio between any two statistical indicators, so we could examine if this difference or ratio is of any statistical significance.

Therefore, we can consider confidence interval also as a measure of a sample and research quality.

Depending on the confidence level that we choose, the interval margins of error also change. In other words: if we select randomly hundred times a sample of 121 individuals and calculate the mean cholesterol concentration and the confidence interval of that estimate in the sample, then in ten out of these hundred samples the confidence interval will not include the actual mean of the population.

It should be kept in mind that a confidence interval is accurate only for the samples that follow a normal distribution, whereas it is approximately accurate for large samples that are not distributed normally.

However, it is important to know the input based on which the confidence interval is calculated, so we could better understand its meaning and interpretation.

Let us go back to our example of cholesterol concentration in the population to see how the confidence interval can be used for estimating statistical significance of the difference between two means. Let us remember what the definition of confidence interval was: it defines margins of error within which we can expect the actual value with 95% confidence. In the last twenty years, every day there are more journals that require reporting of the confidence intervals for each of their key results. Therefore, we can consider confidence interval also as a measure of the sample and research quality. Many journals therefore require providing the key results with respective confidence intervals (4,5). The most used confidence intervals in the biomedical literature are the 90%, 95%, 99% and not so often 99.9% one. The larger the confidence interval is, the higher is the possibility that this interval includes also the mean of cholesterol concentration in the population.

Although there are some other ways of calculating it, the confidence interval is generally and most frequently calculated using standard error. Most often we decide for the 95% confidence what means that we will allow that only in 5% cases the actual mean of population does not fall into our interval.

For small samples (N < 30) the t value should be used instead of the Z value in the formula for confidence interval, with N-1 degrees of freedom (9).

The P value describes probability that the observed phenomenon (deviation) occurred by chance, whereas the confidence interval provides margins of error within which it is possible to expect the value of that phenomenon. Our confidence interval contains also a zero (0) meaning that it is quite possible that the actual value of the difference will equal zero, namely, that there is no difference between the cholesterol concentration between men and women.

Reporting of this confidence interval provides additional information about the sample and the results.

Depending on the confidence level that we choose, the interval margins of error and respective range also change.

For small samples the t value is higher than the Z value what logically means that the confidence interval for smaller samples with the same confidence level is larger. It should become a standard of all scientific journals to report key results with respective confidence intervals because it enables better understanding of the data to an interested reader.

There is a rule for same sized samples: the smaller the confidence level is, the higher is the estimate accuracy. Let us now see how the range of confidence interval and its margins of error change depending on the confidence level in our example of cholesterol concentration estimate in the population (Figure 1).

Many statistical textbooks contain tables with t values for matching confidence level and different degrees of freedom (1). Only the studies with a large sample will give a very small confidence interval, which points to high estimate accuracy with a high confidence level. The P value describes probability that the observed phenomenon (difference) occurred by chance, whereas the confidence interval provides margins of error within which it is possible to expect the value of that phenomenon. In the last twenty years, increasing number of journals require reporting of the confidence intervals for each of their key results. It should become a standard of all scientific journals to report key results with respective confidence intervals because it enables better understanding to the interested reader.

Therefore, we can consider confidence interval also as a measure of a sample and research quality.

Depending on the confidence level that we choose, the interval margins of error also change. In other words: if we select randomly hundred times a sample of 121 individuals and calculate the mean cholesterol concentration and the confidence interval of that estimate in the sample, then in ten out of these hundred samples the confidence interval will not include the actual mean of the population.

It should be kept in mind that a confidence interval is accurate only for the samples that follow a normal distribution, whereas it is approximately accurate for large samples that are not distributed normally.

However, it is important to know the input based on which the confidence interval is calculated, so we could better understand its meaning and interpretation.

Let us go back to our example of cholesterol concentration in the population to see how the confidence interval can be used for estimating statistical significance of the difference between two means. Let us remember what the definition of confidence interval was: it defines margins of error within which we can expect the actual value with 95% confidence. In the last twenty years, every day there are more journals that require reporting of the confidence intervals for each of their key results. Therefore, we can consider confidence interval also as a measure of the sample and research quality. Many journals therefore require providing the key results with respective confidence intervals (4,5). The most used confidence intervals in the biomedical literature are the 90%, 95%, 99% and not so often 99.9% one. The larger the confidence interval is, the higher is the possibility that this interval includes also the mean of cholesterol concentration in the population.

Although there are some other ways of calculating it, the confidence interval is generally and most frequently calculated using standard error. Most often we decide for the 95% confidence what means that we will allow that only in 5% cases the actual mean of population does not fall into our interval.

For small samples (N < 30) the t value should be used instead of the Z value in the formula for confidence interval, with N-1 degrees of freedom (9).

The P value describes probability that the observed phenomenon (deviation) occurred by chance, whereas the confidence interval provides margins of error within which it is possible to expect the value of that phenomenon. Our confidence interval contains also a zero (0) meaning that it is quite possible that the actual value of the difference will equal zero, namely, that there is no difference between the cholesterol concentration between men and women.

Reporting of this confidence interval provides additional information about the sample and the results.

Depending on the confidence level that we choose, the interval margins of error and respective range also change.

For small samples the t value is higher than the Z value what logically means that the confidence interval for smaller samples with the same confidence level is larger. It should become a standard of all scientific journals to report key results with respective confidence intervals because it enables better understanding of the data to an interested reader.

There is a rule for same sized samples: the smaller the confidence level is, the higher is the estimate accuracy. Let us now see how the range of confidence interval and its margins of error change depending on the confidence level in our example of cholesterol concentration estimate in the population (Figure 1).

Many statistical textbooks contain tables with t values for matching confidence level and different degrees of freedom (1). Only the studies with a large sample will give a very small confidence interval, which points to high estimate accuracy with a high confidence level. The P value describes probability that the observed phenomenon (difference) occurred by chance, whereas the confidence interval provides margins of error within which it is possible to expect the value of that phenomenon. In the last twenty years, increasing number of journals require reporting of the confidence intervals for each of their key results. It should become a standard of all scientific journals to report key results with respective confidence intervals because it enables better understanding to the interested reader.

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