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Unusual Results

Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...

Prediction Intervals

Prediction Intervals

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.

Regression Toward the Mean

Regression Toward the Mean

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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Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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Related Experiment Video

Updated: Jan 6, 2026

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Precluding rare outcomes by predicting their absence.

Eric W Schoon¹, David Melamed¹, Ronald L Breiger²

¹Department of Sociology, The Ohio State University, Columbus, Ohio, United States of America.

|October 11, 2019

Summary

Forecasting rare events is challenging due to limited data. This study introduces a novel configurational method to identify conditions that prevent rare outcomes, improving analysis of infrequent events.

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Area of Science:

Social Sciences
Statistics
Data Analysis

Background:

Forecasting extremely rare events presents significant challenges.
Existing methods struggle with multiple causes, insufficient outcome observations, and biased estimates.
Limited data hinders the assessment of model fit for rare outcomes.

Purpose of the Study:

To introduce a novel approach for analyzing rare event data.
To address limitations in forecasting rare outcomes.
To identify conditions that preclude the occurrence of rare events.

Main Methods:

Utilizing configurational methods to analyze rare event data.
Focusing on conditions under which rare outcomes do not occur.
Employing Monte Carlo experiments and bootstrap inferential tests.

Main Results:

The novel approach can systematically preclude up to 78.6% of observations.
Configuration methods identify conditions that would prevent rare outcomes.
Application to ground-truth data yields novel substantive insights.

Conclusions:

The proposed configurational method offers a powerful alternative for analyzing rare event data.
This approach overcomes limitations of standard statistical analyses for infrequent outcomes.
It provides a systematic way to gain insights into the non-occurrence of rare events.