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Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
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Systematic or...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

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Statistical detection of systematic election irregularities.

Peter Klimek1, Yuri Yegorov, Rudolf Hanel

  • 1Section for Science of Complex Systems, Medical University of Vienna, Vienna, Austria.

Proceedings of the National Academy of Sciences of the United States of America
|September 27, 2012
PubMed
Summary
This summary is machine-generated.

Statistical analysis of election results can detect fraud. Higher kurtosis in vote distributions indicates irregularities like ballot stuffing, enabling robust cross-country comparisons of election integrity.

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Area of Science:

  • Political Science
  • Statistics
  • Computational Social Science

Background:

  • Free and fair elections are fundamental to democratic societies.
  • National elections can be viewed as large-scale social experiments.
  • Statistical analysis of polling results can identify election irregularities.

Purpose of the Study:

  • To develop a statistical method for detecting election fraud.
  • To quantify the extent of fraudulent mechanisms in elections.
  • To enable robust cross-country comparisons of election integrity.

Main Methods:

  • Analyzing vote distributions using kurtosis.
  • Developing a parametric model to quantify fraud.
  • Formulating a parametric test for statistical properties in election results.

Main Results:

  • Vote distributions in elections with alleged fraud exhibit significantly higher kurtosis than normal elections.
  • Systematic ballot stuffing explains reported irregularities in recent Russian elections.
  • The developed technique is robust to data resolution, allowing cross-country comparisons.

Conclusions:

  • Statistical analysis of vote distributions, specifically kurtosis, can effectively detect election fraud.
  • The proposed method provides a quantifiable measure of fraudulent mechanisms.
  • This approach offers a reliable tool for assessing election integrity globally.