The degree of certainty associated with an experiment expressed in terms of mathematical probability. Increasing the confidence level requires larger sample sizes.

the likelihood that the sampling error in a survey result will fall within a specified range, usually expressed in terms of standard errors (e.g. 1 standard error equals 68% likelihood; 2 standard errors equals 95% likelihood).

The level of certainty to which an estimate can be trusted. The degree of certainty is expressed as the chance that a true value will be included within a specified range, called a confidence interval.

the percentage of times that a sample would be expected to fall by chance outside the confidence interval; risk level. At the .05 confidence level, only 5 percent of the time would a person's score fall by chance outside the confidence interval. Cp. statistical significance.

A measurement of the likelihood that an inductive generalization will produce a true conclusion. Confidence level indicated the percentage of random samples in which the property in question occurs within the error margin. (Note: do not take confidence level in psychological terms, as a measure of how sure a person happens to feel about some generalization. It is a quantifiable measurement of a generalization's actual application to random samples.)

The probability that a confidence interval includes the true population parameter.

The probability that a confidence interval or region will contain the true parameters is given by the confidence level. A 95% confidence interval will contain the true parameters 95% of the time on average. Usually a confidence level of 95% or greater is considered statistically significant.

The logical strength of the argument; the frequency with which the conclusion would be true if the premises were true.

generally 95% or 99%. At a 95% confidence level, 95 times out of 100 the true value will fall within the confidence interval.

The specific probability of obtaining some result from a sample if it did not exist in the population as a whole, at or below which the relationship will be regarded as statistically significant (Alreck, 444).

The probability of accepting the null hypothesis when it is true - for example the probability that test results will detect disease when the true prevalence is greater than or equal to the specified design prevalence.

An assessment of the probability that an event will occur, usually expressed as a percentage.

The probability that a confidence interval (+/- margin of error) will include the true population value. 95% confidence level is the most commonly used.

A threshold level that must be met by the premise of a rule in order for the conclusion statements to be activated.

The probability (or confidence) with which one makes a statement about an estimate

a statistical measure of the likelihood that an experimental result is "real" and not the result of chance alone. Confidence improves as larger numbers of participants are included in a trial.

The range (with a specified value of uncertainty, usually expressed in percent) within which the true value of a measured quantity exists.

The degree of assurance that a specified failure rate is not exceeded.

A statistical calculation measuring the degree of certainty about a forecast.

Dynamic Logic uses a 90% confidence level. It is the probability that if a metric increases at 90%, the odds are 90 to 10 that the same metric increased among everyone who saw the ad. If a lower confidence level is used, the odds are too high that the difference between the Control and Exposed groups can be attributed to sampling error and not the ad campaign.

A Confidence Level is used during Monte Carlo Simulations to indicate that a certain percentage of the simulated equity curves demonstrated results for a particular measure that were greater than the confidence level. For example, a 95% Confidence Level MAR Ratio of 0.8 indicates that 95% of the simulation iterations showed a MAR Ratio of 0.8 or better.

The fraction of measurements that can be expected to lie within a given range. Thus if m = (15.34 ± 0.18) g, at 67% confidence level, 67% of the measurements lie within (15.34 - 0.18) g and (15.34 + 0.18) g. If we use 2 deviations (±0.36 here) we have a 95% confidence level.

The likelihood that the true value of a variable is within a confidence interval. For example, for confidence intervals at the 95% level, we are statistically 95% certain that the actual value of the variable is within the interval.

A level of assurance that a certain level of failure will not be exceeded.