“Sampling bias” papers · AI for Research

Sampling bias

Stefano Panzeri, Cesare Magri, Ludovico Carraro · Scholarpedia · 2008

Cited by 23Open access

Sampling Bias

Longo, L. · ATE Central (National Science Foundation)

This web-based interactive GIS map resource from MAPS GIS at Lane Community College and created by L. Longo, will provide students with the opportunity to use the GIS map in conjunction with a statistics lesson and &quot;use the data to find basic statistics about samples with the help of spreadsheet software.&quot; In this lesson, students will learn the importance of sampling methods using Oregon population data and then &quot;identify potential bias in each sample.&quot; Included in this module is a student lesson for using ArcServer, a teaching guide, and a link to the web-based map.

Cited by 0

Sampling Bias

Longo, L. · ATE Central (National Science Foundation)

In the activity from Lane Community College's MAPS GIS program, students will use the Oregon Reference Map data to find basic statistics about samples with the help of spreadsheet software. Students will complete this activity in conjunction with an introductory statistics lesson about the importance of samples.Â Lesson objectives includeÂ describing the effect a sample has on calculated statistics, describing geographic patterns, and listing potential biases when using a sample.Â This resource includes both a teaching guide and student lesson.

Cited by 0

Sampling Bias

Mark A. S. McMenamin · Springer geology · 2016

Cited by 1

Sampling bias

Panzeri, S., Magri, C., Carraro, L. · MPG.PuRe (Max Planck Society) · 2008

Sampling bias means that the samples of a stochastic variable that are collected to determine its distribution are selected incorrectly and do not represent the true distribution because of non-random reasons. Let us consider a specific example: we might want to predict the outcome of a presidential election by means of an opinion poll. Asking 1000 voters about their voting intentions can give a pretty accurate prediction of the likely winner, but only if our sample of 1000 voters is 'representative' of the electorate as a whole (i.e. unbiased). If we only poll the opinion of, 1000 white middle class college students, then the views of many important parts of the electorate as a whole (ethnic minorities, elderly people, blue-collar workers) are likely to be underrepresented in the sample, and our ability to predict the outcome of the election from that sample is reduced. In an unbiased sample, differences between the samples taken from a random variable and its true distribution, or differences between the samples of units from a population and the entire population they represent, should result only from chance. If their differences are not only due to chance, then there is a sampling bias. Sampling bias often arises because certain values of the variable are systematically under-represented or over-represented with respect to the true distribution of the variable (like in our opinion poll example above). Because of its consistent nature, sampling bias leads to a systematic distortion of the estimate of the sampled probability distribution. This distortion cannot be eliminated by increasing the number of data samples and must be corrected for by means of appropriate techniques, some of which are discussed below. In other words, polling an additional 1000 white college students will not improve the predictive power of our opinion poll, but polling 1000 individuals chosen at random from the electoral roll would. Obviously, a biased sample may cause problems in the measure of probability functionals (e.g., the variance or the entropy of the distribution), since any statistics computed from that sample has the potential to be consistently erroneous.

Cited by 0

Sampling bias

S. Panzeri, C. Magri, L. Carraro · MPG.PuRe (Max Planck Society) · 2008

Sampling bias means that the samples of a stochastic variable that are collected to determine its distribution are selected incorrectly and do not represent the true distribution because of non-random reasons. Let us consider a specific example: we might want to predict the outcome of a presidential election by means of an opinion poll. Asking 1000 voters about their voting intentions can give a pretty accurate prediction of the likely winner, but only if our sample of 1000 voters is 'representative' of the electorate as a whole (i.e. unbiased). If we only poll the opinion of, 1000 white middle class college students, then the views of many important parts of the electorate as a whole (ethnic minorities, elderly people, blue-collar workers) are likely to be underrepresented in the sample, and our ability to predict the outcome of the election from that sample is reduced. In an unbiased sample, differences between the samples taken from a random variable and its true distribution, or differences between the samples of units from a population and the entire population they represent, should result only from chance. If their differences are not only due to chance, then there is a sampling bias. Sampling bias often arises because certain values of the variable are systematically under-represented or over-represented with respect to the true distribution of the variable (like in our opinion poll example above). Because of its consistent nature, sampling bias leads to a systematic distortion of the estimate of the sampled probability distribution. This distortion cannot be eliminated by increasing the number of data samples and must be corrected for by means of appropriate techniques, some of which are discussed below. In other words, polling an additional 1000 white college students will not improve the predictive power of our opinion poll, but polling 1000 individuals chosen at random from the electoral roll would. Obviously, a biased sample may cause problems in the measure of probability functionals (e.g., the variance or the entropy of the distribution), since any statistics computed from that sample has the potential to be consistently erroneous.

Cited by 0

Sampling bias-HDI.

Matteo Serafino (11599593), Higor S. Monteiro (12400940), Shaojun Luo (12400943) et al. · Figshare · 2022

(A) Probability density function and (B) Cumulative distribution function of the fraction of the population per neighborhood with a given HDI in Fortaleza to the total population. We show the real distributions and the distributions from the apps GPS data. Two-sample KS test indicates that we cannot reject the hypothesis that the real and GPS sample come from the same distribution under the test, indicating lack of sampling bias under this test. (TIFF)

Cited by 0Open access

Sampling bias-coverage.

Matteo Serafino (11599593), Higor S. Monteiro (12400940), Shaojun Luo (12400943) et al. · Figshare · 2022

(A) Probability density function and (B) Cumulative distribution function of the fraction of the population per neighborhood in Fortaleza to the total population. We show the real distributions and the distributions from the apps GPS data. Both distributions pass a two-sample KS test indicating that we cannot reject the hypothesis that they come from the same distribution under the test. (TIFF)

Cited by 0Open access

Sampling bias-gender.

Matteo Serafino (11599593), Higor S. Monteiro (12400940), Shaojun Luo (12400943) et al. · Figshare · 2022

(A) PDF and (B) CDF of gender distribution in the GPS geolocalized data compared with the real patient data suggesting lack of bias. (TIFF)

Cited by 0Open access

Sampling bias-age.

Matteo Serafino (11599593), Higor S. Monteiro (12400940), Shaojun Luo (12400943) et al. · Figshare · 2022

(A) PDF and (B) CDF of age distribution in the GPS geolocalized data compared with the real patient data. We cannot reject the hypothesis that both samples come from the same distribution under KS statistical testing. (TIFF)

Cited by 0Open access

Sampling Bias Awareness and Sampling Behavior

Canel Kahriman, Tajali-Awal, Tara, Berger, Thilo et al. · Open Science Framework · 2025

This research is based on a university course (Exprak): We will examine if bias education will lead to an increase of sampling behaviour and therefore lead to improved decision making. We predict that bias education will increase sampling behaviour and that this increased sampling behaviour will lead to improved decision making.

Cited by 0Open access

introduction or sampling bias?

Mino Caputo, Marco Pombi, João Dinis et al. · 2016

suggests that the study area may be subject to ecological changes with a potential impact on both the genetics of

Cited by 0

Sampling bias and extrapolation.

Tomas Barta (8067509), Lubomir Kostal (44869) · Figshare · 2024

The information-metabolic efficiency calculated by the Jimbo-Kunisawa algorithm is plotted for different numbers of principal components used. We calculated the information-metabolic efficiency from different numbers of trials. At high number of components, lower number of trials lead to significantly higher information-metabolic efficiency. This is the effect of the sampling bias. We attempted to remove the bias by using the quadratic extrapolation method. For 500 principal components the bias is still relatively low, and increasing the number of components brings little benefit in terms of information-metabolic efficiency. (TIF)

Cited by 0Open access

Sampling Bias and Gender

Florence L. Denmark, Talia Zarbiv · The SAGE Encyclopedia of Psychology and Gender · 2017

Cited by 1

Sampling bias in transgender studies

Greta R. Bauer, Ayden I. Scheim · The Lancet Infectious Diseases · 2013

Cited by 37

Maximum entropy method: sampling bias

Jorge Fernández-de-Cossío, Jorge Fernández-de-Cossio-Díaz · arXiv (Cornell University) · 2015

Maximum entropy method is a constructive criterion for setting up a probability distribution maximally non-committal to missing information on the basis of partial knowledge, usually stated as constrains on expectation values of some functions. In connection with experiments sample average of those functions are used as surrogate of the expectation values. We address sampling bias in maximum entropy approaches with finite data sets without forcedly equating expectation values to corresponding experimental average values. Though we rise the approach in a general formulation, the equations are unfortunately complicated. We bring simple case examples, hopping clear but sufficient illustration of the concepts.

Cited by 0Open access

Bias in Stabilized Sieve Sampling

Liming Guan, John P. Wendell · Journal of Modern Applied Statistical Methods · 2009

The stabilized sieve sample selection method (SSM) is considered to be a probability proportional to size (PPS) sampling method with an unbiased estimator (Horgan 1997, 1998). This article demonstrates that SSM does not select items with PPS and that the point estimator is biased.

Cited by 0Open access

Sampling Bias and Scorecard Evaluation

Stefan Lessmann · 2023

Cited by 0

Sampling Bias in Entrepreneurial Experiments

Ruiqing Cao, Rembrand Koning, Ramana Nanda · National Bureau of Economic Research · 2021

Using data from a prominent online platform for launching new digital products, we document that 'sampling bias'-defined as the difference between a startup's target customer base and the actual sample on which early 'beta tests' are conducted-has a systematic and persistent impact on the venture's success. Specifically, we show that products with a female-focused target market launching on a typical day, when nine in ten users on this platform are men, experience 45% less growth a year after launch than those for whom the target market is more male-focused. By isolating exogenous variation in the composition of beta testers unrelated to the characteristics of launched products on that day, we find that on days when there are unexpectedly more women beta testers on the platform-reducing the amount of sampling bias for female-focused products -the gender-performance gap shrinks towards zero. Our results highlight how sampling bias can lead to fewer successfully commercialized innovations for consumers who are underrepresented among early users.

Cited by 6Open access

Nutcracker, neurosis, or sampling bias?

R M Valori · Gut · 1990

Cited by 30Open access

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