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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Confidence Intervals01:21

Confidence Intervals

8.4K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Related Experiment Video

Updated: Oct 26, 2025

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
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Prediction intervals reporting in orthodontic meta-analyses.

Jadbinder Seehra1, Daniel Stonehouse-Smith1, Nikolaos Pandis2

  • 1Department of Orthodontics, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, Guy's Hospital, Guy's and St Thomas NHS Foundation Trust, UK.

European Journal of Orthodontics
|July 31, 2021
PubMed
Summary
This summary is machine-generated.

Prediction intervals are rarely reported in orthodontic meta-analyses, despite their clinical importance for interpreting heterogeneity. Routine reporting is advocated to better understand treatment intervention effects.

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Area of Science:

  • Orthodontics
  • Biostatistics
  • Evidence-based dentistry

Background:

  • Prediction intervals offer clinical insights into heterogeneity in meta-analyses.
  • Understanding the prevalence of prediction interval reporting in orthodontic research is crucial.

Purpose of the Study:

  • To determine the reporting frequency of prediction intervals in orthodontic random effects meta-analyses.
  • To assess the corroboration between prediction intervals and 95% confidence intervals (CIs) in effect size estimates.

Main Methods:

  • Systematic reviews with random effects meta-analyses (≥3 trials) published between 2010-2021 were identified.
  • Prediction intervals were calculated, and their impact on interpretation compared to 95% CIs was analyzed.
  • Fisher's exact test examined factors associated with prediction interval reporting.

Main Results:

  • Prediction intervals were reported in only 19.0% of the 121 included meta-analyses.
  • In 63.3% of cases, prediction intervals contradicted statistically significant findings indicated by 95% CIs.
  • Only 6.3% of prediction intervals corroborated the 95% CIs.

Conclusions:

  • Reporting of prediction intervals in orthodontic meta-analyses is infrequent, likely due to lack of awareness.
  • Advocating for prediction interval reporting in future orthodontic random effects models is recommended.
  • Prediction intervals provide essential information on the range of expected treatment intervention effects.