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  2. Interval estimation - Wikipedia

    en.wikipedia.org/wiki/Interval_estimation

    In statistics, interval estimation is the use of sample data to estimate an interval of possible values of a parameter of interest. This is in contrast to point estimation, which gives a single value. [1] The most prevalent forms of interval estimation are confidence intervals (a frequentist method) and credible intervals (a Bayesian method). [2]

  3. Prediction interval - Wikipedia

    en.wikipedia.org/wiki/Prediction_interval

    The prediction interval is conventionally written as: [, +]. For example, to calculate the 95% prediction interval for a normal distribution with a mean (μ) of 5 and a standard deviation (σ) of 1, then z is approximately 2. Therefore, the lower limit of the prediction interval is approximately 5 ‒ (2⋅1) = 3, and the upper limit is ...

  4. Confidence interval - Wikipedia

    en.wikipedia.org/wiki/Confidence_interval

    The blue intervals contain the population mean, and the red ones do not. This probability distribution highlights some different confidence intervals. Informally, in frequentist statistics, a confidence interval (CI) is an interval which is expected to typically contain the parameter being estimated.

  5. Estimation statistics - Wikipedia

    en.wikipedia.org/wiki/Estimation_statistics

    The confidence interval summarizes a range of likely values of the underlying population effect. Proponents of estimation see reporting a P value as an unhelpful distraction from the important business of reporting an effect size with its confidence intervals, [7] and believe that estimation should replace significance testing for data analysis ...

  6. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    A great advantage of bootstrap is its simplicity. It is a straightforward way to derive estimates of standard errors and confidence intervals for complex estimators of the distribution, such as percentile points, proportions, Odds ratio, and correlation coefficients.

  7. Estimation - Wikipedia

    en.wikipedia.org/wiki/Estimation

    Estimation (or estimating) is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable. The value is nonetheless usable because it is derived from the best information available. [ 1 ]

  8. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    If the region does comprise an interval, then it is called a likelihood interval. [ 16 ] [ 18 ] [ 22 ] Likelihood intervals, and more generally likelihood regions, are used for interval estimation within likelihoodist statistics: they are similar to confidence intervals in frequentist statistics and credible intervals in Bayesian statistics.

  9. Statistics - Wikipedia

    en.wikipedia.org/wiki/Statistics

    Confidence intervals: the red line is true value for the mean in this example, the blue lines are random confidence intervals for 100 realizations. Most studies only sample part of a population, so results do not fully represent the whole population. Any estimates obtained from the sample only approximate the population value.