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The difference between the upper and lower confidence limits.
(CI) A range of values within which a result is expected to fall with a specific probability.
A number (range) that shows how likely it is that the true amount is inside the listed range of amounts; for example, a 95% confidence interval of 25-45 would mean there is a 95% chance that the right amount (number, score, measurement) is somewhere between 25 and 45.
A range of numbers around a mean, proportion, or rate that serves as a rough guide or minimum estimate of the inherent uncertainty in a epidemiologic result.
A range of possible result, typically expressed at 95%. The narrower (smaller) a range is, the more reliable the results.
A range of values constructed around a point estimate that makes it possible to state that an interval contains the population parameter between its upper and lower confidence limits. The most frequently used confidence interval is the 95% confidence interval. This can be interpreted as there is only a 5% chance that the sample is so extreme that the 95% confidence interval calculated will not cover the population mean.
A range of values used within which a true value is expected to fall 95% of the time.
an estimated range of values with a given high probability of covering the true population value
an interval estimate with a specific level of confidence
an interval within which the mean of a population probably lies
a range around a measurement that conveys how precise the measurement is
a range defined between a lower and an upper bound, which quantifies the percentage of time that this range contains the parameter of interest
a range of scores in which the researcher can expect some reasonable proportion of the observed scores to fall
a range of values that has a specified probability of containing the parameter being estimated
a range of values within which we are willing to assert with a specified level of confidence that an unknown parameter value lies
a range whose endpoints define a certain percentage of the responses to a question
a standard way of expressing our uncertainty about the value of a population parameter
a statistical technique that establishes a range of scores within which the true score would likely fall
The confidence interval, or confidence band, is marked by two points that define with specified probability the range that includes an individual's true score.
The range under which a specified percentage of cases fall. For example, if a mean has a standard deviation of 1, there is 68% confidence that a score will fall within one standard deviation of the mean.
The expected range in which an actual population value will be found, at a given level of confidence or probability
Estimate of the spread between the lowest likely result (lower confidence limit) and the highest likely result (upper confidence limit). The true result of the study probably lies somewhere within this confidence interval.
A range around a measurement conveying the amount of precision. In general, the wider the range, the less precise the number.
A confidence interval quantifies the level of uncertainty in measurement or estimate. A 95% confidence interval is the range of values within which we can be 95% sure that the ‘true' value of a statistical measure for the whole population lies.
An interval around a sample mean within which the population mean is likely to fall. In common practice, the largest value of the interval is 2 standard errors above the mean and the smallest value is 2 standard errors below it.
the range of values that a population parameter could take at a given level of significance.
A measure of the statistical precision (stability of the estimate) of an observed effect size. It is usually expressed as the 95% confidence interval around the point estimate. For example, the effect of estrogen on the relative risk of endometrial cancer may be expressed as 7 (95% CI, 6 to 10), meaning that the relative risk interval between 6 and 10 has a 95% probability of containing the true estimate of risk
A confidence interval is the range of values that will include the population parameter based on information from a single sample of the population. The population estimate from a sample is only one of the many estimates possible from all samples of the same size drawn from the same population. The sampling error or standard error estimates the amount of variation in the sample estimates. Random errors follow a normal (bell-shaped) distribution and therefore, the proportion of observations within segments can be calculated. For example, 95% of all the values are in a range that is 1.96 standard units on either side of the midpoint. A 95% confidence interval indicates that there is a 95% chance that the interval contains the actual population value.
An estimate of a population parameter that consists of a range of values bounded by statistics called upper and lower confidence limits, within which the value of the parameter is expected to be located.
A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data. The width of the confidence interval gives us some idea about how uncertain we are about the unknown parameter. A very wide interval may indicate that more data should be collected before anything very definite can be said about the parameter.
A numeric interval in which the true number obtained for a population from a sample falls. The interval is stated with a degree of probability, typically 95 percent. For example, if a survey using a sample of 300 found a 25 percent effect, the confidence interval is 20 percent - 30 percent (25 plus or minus 5), meaning that there is a 95 percent certainty that the true percentage in the overall population falls within that inclusive range.
An interval indicating numerically the range of uncertainty for the probability of a particular outcome. A wide confidence interval, for example, 5-50 percent, represents considerable statistical uncertainty about the probability of the outcome to occur.
the limits in a sample distribution between which one expects with a particular degree of confidence that the population value will lie; specifically, the distance in standard deviation units from the mean that determines such limits.
An estimated range of values, calculated from data, which is likely to include a particular unknown population parameter. The confidence level (e.g. 95%) says that if an infinite number of such sample s were taken from the same population, then 95% of them would give confidence intervals which include the actual value of the parameter.
the confidence interval gives a range in which, at a certain probability (usually 95% or 99%), you can be confident that the real response from the study group would lie. This is a function of the size of your sample and the size of the population you are studying, and is usually expressed as +/- from the responses you got. For example, if a sample size of 363 and a study group population of 2500 produced a confidence interval of 4.7% at the 95% confidence level, that would mean that if on a given question you learned that 45% of the sample gave blood in the past year, you could be 95% certain that if all 2500 people in the population had been surveyed, the percentage who stated that they had given blood in the last year would lie between 40.3% (45%-4.7%) and 49.7% (45%+4.7%). [For more discussion of this, click here.] Note: this assumes that the sample was randomly generated. There can be other errors from the sampling generated by patterns of non-response and nonrandom samples, validity problems, and reliability problems.
A random interval constructed from data in such a way that the probability that the interval contains the true value of the parameter can be specified before the data are collected. An interval computed from sample values. Intervals so constructed will straddle the estimated parameter a certain percentage of the time in repeated sampling.
A numeric range estimated with a specific degree of confidence or probability to include a value. Conventionally reported confidence intervals are ranges in which the actual value of the estimated variable can be expected to fall 95 or 99 percent of the time, corresponding to probabilities that a difference or significant result is due to chance of less than 5 percent and 1 percent, respectively (p .05; p .01). In reporting quantified results throughout this report (e.g., odds ratios, relative risks), if the confidence interval is not given, point estimates have at least a 95 percent probability of being statistically significant. Confidence intervals are given for findings reported with lesser levels of statistical significance.
Range (upper and lower limit) constructed so that similar ranges are likely to contain the true mean for a set of observations. Traditionally, CI 95 is the calculated range where 95% of similarly constructed ranges include the true average. {CI 95 = X Â± 1.96 * [SD / SQRT (n)]} NB: The confidence interval is not the range that has a 95% chance of containing the mean â€“ despite this being a commonly reported interpretation. Small confidence intervals are preferred. They can be obtained by having (1) a small variance, (2) a large sample size, or (3) a lower confidence level, e.g., 90% rather than 95%. When a confidence interval does not contain the value 0.0 for differences between groups or 1.0 for odds ratios, it is equivalent to a statistically significant test for difference.
a calculated range of values known to contain the true parameter of interest over the average of repeated trials with specific certainty (probability)
a measure of an estimate's reliability
a measure of an estimate's variability
a measure of the uncertainty (due to the play of chance) associated with that estimate
an attempt to quantify the accuracy of the estimate
an expression of how well an estimate from a particular sample represents an unknown population value
an interval, calculated from the sample data that is very likely to cover the unknown mean, variance, or proportion
an interval centered on the runtime prediction within which the
an interval estimate combined with a probability statement
an interval used to estimate the likely size of a population parameter
an interval whose endpoints are statistics (numbers calculated from a random sample) whose purpose is to estimate a parameter (a number that could, in theory, be calculated from the population, if measurements were available for the whole population)
a notion from probability
a number used in statistics which provides the level of confidence that the results of the study are valid
a range about a given estimator that has a specified probability of containing the average of the estimates for the parameter derived from all possible samples of the same size and design
a range about a given estimator that has a specified probability of containing the result of a complete enumeration
a range of numbers based on the data of the sample believed to include the population parameter we are interested in
a range of plausible values that accounts for uncertainty in a statistical estimate
a range of values that describes
a range of values that tries to quantify this uncertainty
a range of values that, with some level of certainty, contains the true value the estimate is approximating
a range of values which span from the Lower Confidence Limit to the Upper Confidence Limit
a range that would include the average result of all possible samples with a known probability
a range which contains our estimate with a specified probability
a range which contains the actual estimate with a specified probability
a range within which, assuming there are no biases in the study method, the true value for the population parameter might be expected to lie
frequentist interval estimator having a guaranteed ( aleatory) probability of including the true population parameter. cf. confidence interval.
statistic constructed from sample data to provide an interval estimate of a population parameter. For example, the average of a sample is generally a good estimate of the population mean. However, another sample chosen from the same sample would almost certainly have a different value. If the sample size is large enough, both of these estimates should be close to each other and the population mean. The confidence interval provides an interval or range of values around the estimate. For one parameter, such as the mean, standard deviation, or probability level, the most common intervals are or two sided (i.e. the statistic is between the lower and upper limit) and one sided (i.e. the statistic is smaller or larger than the end point). For two or more parameters, a confidence region, the generalization of a confidence interval, can take on arbitrary shapes. For sensitivity testing, the smallest confidence regions are oval shapes.
range of values that is formed to contain within its boundaries, with a predetermined level of confidence, the population value being estimated.
a range for the true value, based on the relative sizes of the sample and the population. If you have computed that 93% of customers are very satisfied, 5%, the actual percentage is expected to be between 88% and 98%.
A research study or experiment can show absolutely only the outcomes or results for the study participants themselves; the study results may not be true for others. The confidence interval (CI) is a mathematical description of how likely it is that others will have the same result as the study participants. The CI occurs between a lowest possible limit and a highest possible limit. The narrower the range between these two limits, the greater the likelihood that the results of the study can be applied to others.
The range around a numeric statistical value obtained from a sample, within which the actual, corresponding value for the population is likely to fall, at a given level of probability (Alreck, 444).
An interval that is believed, with a preassigned degree of confidence, to include the particular value of some parameter being estimated.
calculation that helps show the statistical precision of a rate. It shows the most likely range of the "true" rate—a wide confidence interval indicates the uncertainty stemming from small numbers; a narrow interval reflects a high degree of statistical confidence.
Range of values for a rate that will include the true value of the rate a given percentage of the time. Example: 95% CI includes the true value of the rate 95% of the time.
a statement of accuracy based on a statistic whose distribution function is known, for example, the normal distribution function or the bivariate normal distribution function. Probable errors are stated as the difference between 100% and some percentage of confidence. For example, if probable error is 5%, then the result is stated as being "at the 95% confidence level."
A measure of sampling error. A 95 per cent confidence interval for an estimate is the range which will contain the 'true' figure on average 19 times out of 20. Note that this 'true' value may still be affected by systematic errors present in the survey and analysis processes
A range of values inferred or believed to enclose the actual or true value of an uncertain quantity with a specified degree of probability. Confidence intervals may be inferred based upon sampling distributions for a statistic.
An agencyâ€™s rate is calculated from a sample of patients at one point in time. If we were to repeatedly pick a different sample of patients, or repeatedly collect the data at different points in time, we would likely get different rates-some higher and some lower. The confidence interval takes into account this â€œerrorâ€ in measurement. It is a range of scores that has a certain probability of containing the â€œtrueâ€ score. For example, a 90% confidence interval around an agencyâ€™s score has a 90% chance of containing the â€œtrueâ€ score for that agency. Note: the width of the confidence interval varies with the denominator size (the smaller the denominator, the wider the confidence interval).
(CI): The range of numerical values in which we can be confident (to a computed probability, such as 90 or 95%) that the population value being estimated will be found. Confidence intervals indicate the strength of evidence; where confidence intervals are wide, they indicate less precise estimates of effect. The larger the trial's sample size, the larger the number of outcome events and the greater becomes the confidence that the true relative risk reduction is close to the value stated. Thus the confidence intervals narrow and "precision" is increased. In a "positive finding" study the lower boundary of the confidence interval, or lower confidence limit, should still remain important or clinically significant if the results are to be accepted. In a "negative finding" study, the upper boundary of the confidence interval should not be clinically significant if you are to confidently accept this result. ( Harm, Therapy). To Calculation
depicts the range of uncertainty about an estimate of a treatment effect. It is calculated from the observed differences in outcomes of the treatment and control groups and the sample size of a study. The confidence interval is the range of values above and below the point estimate that is likely to include the true value of the treatment effect. The use of confidence intervals assumes that a study provides one sample of observations out of many possible samples that would be derived if the study were repeated many times. Investigators typically use confidence intervals of 90%, 95%, or 99%. For instance, there is a 95% probability that a 95% confidence interval calculated from a particular study includes the true value of a treatment effect. If the interval includes a null treatment effect (usually 0.0 but 1.0 if the treatment effect measure used is an odds ratio or relative risk), the null hypothesis of no true treatment effect cannot be rejected.
An indication of the accuracy of the results of the survey.
A 1- confidence interval for an unknown parameter is an interval of possible values of the parameter, based on sample data.  It has the property that, in repeated sampling, 100(1-)% of the intervals obtained will contain the true value.
A confidence interval refers to the statistical likelihood that a score falls within a given range around an estimate. This is important because nearly all health care quality scores are developed using a statistical sampling method, which means that there is some uncertainty about whether the sample reflects the population. The confidence interval tells you how confident you can be that the score for the sample represents the score for the entire membership or population. For instance, a 99-percent confidence interval means that, if you drew numerous samples, 99 percent of the estimates would fall within the given range. A narrower range (e.g., a 90-percent confidence interval) would give you less certainty about the estimate.
A range of scores in which it is likely that the student's true score will fall; constructed by means of the standard error of measurement.
A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable. The specified probability is called the confidence level, and the end points of the confidence interval are called the confidence limits.
Also known as a margin of error, this is a measurement of the accuracy of the results of a survey. Example: A margin of error of plus or minus 3.5% means that there is a 95% chance that the responses of the target population as a whole would fall somewhere between 3.5% more or 3.5% less than the responses of the sample (a 7% spread). However, for any specific question, the margin of error could be greater or less than plus or minus 3.5%.
the CI quantifies the uncertainty in measurement; usually reported as 95% CI, which is the range of values within which we can be 95% sure that the true value for the whole population lies.
Confidence intervals are the range of predicted values within a stated degree of confidence (i.e, 95%) that the true parameter will fall. E-F-G-H
The interval that contains the true prevalence (which we can only estimate) 95% of the time. See Methods for more explanation.
A range of values intended to include a parameter
A measure of the degree of (un)certainty of an estimate. Usually presented as a percentage. For example, a confidence level of 95% applied to an upper and lower bound of an estimate indicates there is a 95% chance the estimate lies between the specified bounds. Confidence limits can be calculated for some forms of uncertainty (see Knowledge uncertainty), or estimated by an expert (see Judgement).
A confidence interval provides an estimated range of values in which an actual data value is likely to fall. The smaller the size of a sample, the less accurate are estimates, so the wider the confidence interval. A few datasets on Neighbourhood Statistics indicate confidence intervals, in the form of lower and upper limits to the interval. For example, the life expectancy datasets. The most commonly used confidence interval is 95 per cent. This means that, across the dataset as whole, the confidence intervals are expected to contain the true values around 95 per cent of the time.
A formula that tells us how to use sample data to calculate an interval that estimates a population parameter.
A range of values that contains a parameter value with given probability. A 99% confidence interval gives a range that contains the true value with 99% confidence.
The 100(1-Î±)% confidence interval is an interval for which approximately 100(1-Î±)% of similarly constructed intervals (for a large number of independent samples) will contain the parameter being estimated. Usual values of Î± are 0.1 (90% confidence level), 0.05 (95% confidence level) and 0.01 (99% confidence level).
An upper and lower limit, within which you have a stated level of confidence that the true mean lies. These are usually quoted as 95% intervals, which are constructed so that you are 95% confident that the true mean lies between the limits. To be 99% sure, you need a wider confidence interval. Increased confidence is bought at the cost of precision.
The Confidence Interval (CI) provides a way of measuring how confident we are in the accuracy of the rate. We have used 95% CI which means that we have 95% confidence that the rate falls between the two Confidence Limits. When the CI of two rates overlap then there is no significant difference between the rates. For details of how the CI is calculated please contact stats.team@cancer.org.uk.
A confidence interval is a method of bracketing a measured mean of any parameter with the probability that the bracket contains the true mean of the parameter. The Standard Error of Estimate and Confidence Coefficient are used to define the brackets.
Confidence interval is the range within which the true size of effect (never exactly known) lies with a given degree of assurance. People often speak of a "95% confidence interval" (or "95% confidence limits"). This is the interval which includes the true value in 95% of cases.
upper and lower bounds around an expected value that will, within a certain percentage of confidence, include the expected value. [SEMATECH
Quantifies the uncertainty in measurement. Usually reported as 95 % CI, which is the range of values within which one can expect to find the true value in 95 % of cases.
A range of values ( a1 a2) determined from a sample of definite rules so chosen that, in repeated random samples from the hypothesized population, an arbitrarily fixed proportion of that range will include the true value, x, of an estimated parameter. The limits, a1 and a2, are called confidence limits; the relative frequency with which these limits include is called the confidence coefficient; and the complementary probability is called the confidence level. As with significance levels, confidence levels are commonly chosen as 0.05 or 0.01, the corresponding confidence coefficients being 0.95 or 0.99. Confidence intervals should not be interpreted as implying that the parameter itself has a range of values; it has only one value, . On the other hand, the confidence limits ( a1, a2) being derived from a sample, are random variables, the values of which on a particular sample either do or do not include the true value of the parameter. However, in repeated samples, a certain proportion of these intervals will include provided that the actual population satisfied the initial hypothesis.
A range determined by variability in data, within which there is a specified (usually 95%) chance that a calculated parameter (e.g. relative risk) is thought to lie.
The range either side of the sample mean within which we are confident that the population mean will lie. Usually this is reported at the 95% confidence level, in other words we are sure that if we took a 100 similar samples then the mean would fall into this range 95 times. Or more simply, we are 95% sure that the population mean falls in this range.
A range that contains the true population prevalence estimate a specified percentage of the time, if repeated sampling of the population were performed. The 95% confidence interval (CI) is a range that contains the true population estimate 95% of the time. A smaller range indicates an estimate that is more precise. Small sample sizes or cells with low numbers generate less precise estimates and will have wider confidence intervals. (Derived from TalkingQuality.gov and BRFSS site http://www.cdc.gov/brfss)
The range of values that has a probability x of including the true value for the population. A 95% confidence interval includes the true value for the population 95% of the time.
A range describing where the true population parameter lies with a certain degree of confidence. For example, a 95% confidence interval for the mean estimates that the true mean lies within the confidence interval with 95% confidence (with 5% alpha risk).
A range of values, within which we are fairly sure the true value of the parameter being investigated lies. A common confidence interval (CI) is 95%. Thus, for example, we can be 95% certain that the true population mean lies approximately within the interval calculated from the sample mean ± 2 x standard error of the mean. 2 is an approximation, dependent on sample size.
A range of values that has a specified probability of containing the rate or trend. The 95% (p-value = .05) and 99% (p-value = .01) confidence intervals are the most commonly used.
In reporting the results of a survey, the confidence interval (sometimes called the sample error) is the amount of variation of the result of a sample from the actual result for the entire population of interest. For example, a confidence interval of plus or minus 4% means that if a survey sample response is 52%, then the actual value for the entire population would be expected to be between 48% and 56%.
The probability, based on statistics, that a number will be between an up per and lower limit.
The computed interval with a specified probability (by convention, usually 95%) that the true value of a population parameter is contained within the interval.
An interval of results that is 95 percent likely to contain the true number; equals reported proportion plus or minus the margin of error. (For an alternative explanation).
A measure of the range of probable parameters attributable to the sample design (estimate plus or minus the standard error). The BLS standard is generally the 90 percent level of confidence.
A range of values that have at least the specified probability of containing the parameter value being estimated.
The interval computed from sample data that has a given probability that the unknown parameter, such as the mean or proportion, is contained within the interval. Common confidence intervals are 90%, 95%, and 99%.