Search

The sex ratio is nearly equal in most organisms, while several ecological and evolutionary factors skew the sex ratio. We evaluated the sex ratios of 12 species in the freshwater snail genus Semisulcospira in and around Lake Biwa. We found highly female-biased sex ratios in all 12 species. The sex ratios were nearly equal or slightly female-biased in the smaller shell-size classes, while they were highly female-biased in the larger size classes. We also found that females were significantly larger in most of these species. Further, the species with a larger size difference between the sexes tended to exhibit a more female-biased sex ratio. Considering the general correlation between shell size and age, this pattern can be explained by longer lifespans in females, shaping the observed female-biased sex ratios. We also found that some Semisulcospira species showed female-biased sex ratios in all shell size classes. This pattern suggests that the distinct longevity hypothesis can not solely explain the observed biased sex ratios. We discussed the possible contributions of the sampling errors and selfish genetic elements to the observed biased sex ratios. This study provides essential information to disentangle the mechanisms of the biased sex ratio in the Semisulcospira snails. [ABSTRACT FROM AUTHOR]

The random batch method provides an efficient algorithm for computing statistical properties of a canonical ensemble of interacting particles. In this work, we study the error estimates of the fully discrete random batch method, especially in terms of approximating the invariant distribution. The triangle inequality framework employed in this paper is a convenient approach to estimate the long-time sampling error of the numerical methods. Using the triangle inequality framework, we show that the long-time error of the discrete random batch method is |$O(\sqrt {\tau } + e^{-\lambda t})$|⁠ , where |$\tau $| is the time step and |$\lambda $| is the convergence rate, which does not depend on the time step |$\tau $| or the number of particles |$N$|⁠. Our results also apply to the McKean–Vlasov process, which is the mean-field limit of the interacting particle system as the number of particles |$N\rightarrow \infty $|⁠. [ABSTRACT FROM AUTHOR]

When starting an N -body simulation of an isolated galaxy, it is desirable to select particles from a distribution function to ensure that the model is in equilibrium. Random sampling from a DF is widely used, but results in a set of particles that differs by shot noise from that intended. This paper presents a method to reduce sampling noise that has been developed by the author in a many collaborations over a number of years. The technique has been partly described in past papers, though the ideas have not previously been gathered together, nor have its advantages been clearly demonstrated in past work. Of course, sampling errors can also be reduced by a brute force increase in the number of particles, but methods to achieve the same effect with fewer particles have obvious advantages. Here we not only describe the method, but also present three sets of simulations to illustrate the practical advantages of reducing sampling error. The improvements are not dramatic, but are clearly worth having. [ABSTRACT FROM AUTHOR]

Policy makers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner's curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner's curse biases our estimates for selected options upward and can invalidate conventional confidence intervals. This article develops estimators and confidence intervals that eliminate this winner's curse. We illustrate our results by studying selection of job-training programs based on estimated earnings effects and selection of neighborhoods based on estimated economic opportunity. We find that our winner's curse corrections can make an economically significant difference to conclusions but still allow informative inference. [ABSTRACT FROM AUTHOR]

Populations suffer two types of stochasticity: demographic stochasticity, from sampling error in offspring number, and environmental stochasticity, from temporal variation in the growth rate. By modelling evolution through phenotypic selection following an abrupt environmental change, we investigate how genetic and demographic dynamics, as well as effects on population survival of the genetic variance and of the strength of stabilizing selection, differ under the two types of stochasticity. We show that population survival probability declines sharply with stronger stabilizing selection under demographic stochasticity, but declines more continuously when environmental stochasticity is strengthened. However, the genetic variance that confers the highest population survival probability differs little under demographic and environmental stochasticity. Since the influence of demographic stochasticity is stronger when population size is smaller, a slow initial decline of genetic variance, which allows quicker evolution, is important for population persistence. In contrast, the influence of environmental stochasticity is population‐size‐independent, so higher initial fitness becomes important for survival under strong environmental stochasticity. The two types of stochasticity interact in a more than multiplicative way in reducing the population survival probability. Our work suggests the importance of explicitly distinguishing and measuring the forms of stochasticity during evolutionary rescue. [ABSTRACT FROM AUTHOR]

Motivation Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. Results We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest. Availability and implementation All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire. [ABSTRACT FROM AUTHOR]

We present a generalized estimator for the autocorrelation function, S-ACF, which is an extended version of the standard estimator of the autocorrelation function (ACF). S-ACF is a versatile definition that can robustly and efficiently extract periodicity and signal shape information from a time series, independent of the time sampling and with minimal assumptions about the underlying process. Calculating the autocorrelation of irregularly sampled time series becomes possible by generalizing the lag of the standard estimator of the ACF to a real parameter and introducing the notion of selection and weight functions. We show that the S-ACF reduces to the standard ACF estimator for regularly sampled time series. Using a large number of synthetic time series, we demonstrate that the performance of the S-ACF is as good or better than commonly used Gaussian and rectangular kernel estimators, and is comparable to a combination of interpolation and the standard estimator. We apply the S-ACF to astrophysical data by extracting rotation periods for the spotted star KIC 5110407, and compare our results to Gaussian process (GP) regression and Lomb–Scargle (LS) periodograms. We find that the S-ACF periods typically agree better with those from GP regression than from LS periodograms, especially in cases where there is evolution in the signal shape. The S-ACF has a wide range of potential applications and should be useful in quantitative science disciplines where irregularly sampled time series occur. A python implementation of the S-ACF is available under the MIT license. [ABSTRACT FROM AUTHOR]

Nested sampling (NS) is a popular algorithm for Bayesian computation. We investigate statistical errors in NS both analytically and numerically. We show two analytic results. First, we show that the leading terms in Skilling's expression using information theory match the leading terms in Keeton's expression from an analysis of moments. This approximate agreement was previously only known numerically and was somewhat mysterious. Second, we show that the uncertainty in single NS runs approximately equals the standard deviation in repeated NS runs. While intuitive, this was previously taken for granted. We close by investigating our results and their assumptions in several numerical examples, including cases in which NS uncertainties increase without bound. [ABSTRACT FROM AUTHOR]

Motivation Genes evolve under processes such as gene duplication and loss (GDL), so that gene family trees are multi-copy, as well as incomplete lineage sorting (ILS); both processes produce gene trees that differ from the species tree. The estimation of species trees from sets of gene family trees is challenging, and the estimation of rooted species trees presents additional analytical challenges. Two of the methods developed for this problem are STRIDE, which roots species trees by considering GDL events, and Quintet Rooting (QR), which roots species trees by considering ILS. Results We present DISCO+QR, a new approach to rooting species trees that first uses DISCO to address GDL and then uses QR to perform rooting in the presence of ILS. DISCO+QR operates by taking the input gene family trees and decomposing them into single-copy trees using DISCO and then roots the given species tree using the information in the single-copy gene trees using QR. We show that the relative accuracy of STRIDE and DISCO+QR depend on the properties of the dataset (number of species, genes, rate of gene duplication, degree of ILS and gene tree estimation error), and that each provides advantages over the other under some conditions. Availability and implementation DISCO and QR are available in github. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

In sample surveys that collect information on skewed variables, it is often desirable to assess the influence of sample units on the sampling error of survey-weighted estimators of finite population parameters. The conditional bias is an attractive measure of influence that accounts for the sampling design and the estimation method. It is defined as the design expectation of the sampling error conditional on a given unit being selected in the sample. The estimation of the conditional bias is relatively straightforward for simple sampling designs and estimators. However, for complex designs or complex estimators, it may be tedious to derive an explicit expression for the conditional bias. In those complex surveys, variance estimation is often achieved through replication methods such as the bootstrap. Bootstrap methods of variance estimation are typically implemented by producing a set of bootstrap weights that is provided to users along with the survey data. In this article, we show how to use these bootstrap weights to obtain an estimator of the conditional bias. Our bootstrap estimator is evaluated in a simulation study and illustrated using data from the Canadian Survey of Household Spending. [ABSTRACT FROM AUTHOR]

Background: Laboratory sampling is a significant source of error in feed testing. Proficiency testing programs such as the Association of American Feed Control Officials Proficiency Testing Program are an effectivemeans of assessing error in and among analyticalmethods. However, all proficiency test items are comminuted and blended to control variability among items, effectivelyminimizing sampling error. Currently there is nomechanismformonitoring sampling error among laboratories. Objective: The objective of this work was to investigate the feasibility of a proficiency testing program for laboratory sampling methods and provide insight into a program to advance the performance of sampling in laboratories. Methods: The study involved the fabrication of identical feed test items from feed ingredients and shipping the uncomminuted materials to volunteer laboratories. The volunteer laboratories followed in-house procedures for selecting test portions for routine feed tests. Tests on all the test portions for a single analyte were performed by a single laboratory, so that the variability in test results could be attributed to laboratory sampling processes to select test portions. Results: The average RSD, %, for Item A and Item B, respectively, were as follows: protein, 5.08 and 5.23; non-protein nitrogen, 8.90 and 16.6; crude fat, 3.45 and 5.67; vitamin A, 33.9 and 26.9; calcium, 21.9 and 23.6; zinc, 17.9 and 27.9; and copper, 17.4 and 27.9. Conclusion: This study suggests that a proficiency testing program for laboratory sampling is feasible with manual manufacture of the test items, and data can be used to monitor laboratory sampling proficiency and also to compare the performance of different laboratory sampling methods. Highlights: The data illustrates that each analyte has unique distributional and compositional heterogeneity, thus unique sampling error, even when multiple analytes are determined from a single test portion. [ABSTRACT FROM AUTHOR]

Failure of individuals to voluntarily observe regulations and recommendations around mitigating COVID-19 (e.g. social distancing; frequent handwashing) is often cited as a reason why some countries struggled to curtail the spread of the virus. Understanding the factors that are associated with people's willingness to comply with COVID-19 regulations and recommendations is an important step in helping policy makers and health officials reduce the impact of this (and future) pandemics. In the current study we examined this question in one of the countries hardest hit by the pandemic: Spain. A large, representative survey (N = 2100) revealed that several factors were positively associated with willingness to comply to COVID-19 regulations and recommendations. In decreasing order of predictive value, these were: (i) perceptions of whether friends and family were complying (i.e. norms), (ii) trust in science as a basis for lawmaking, (iii) perceived effectiveness of regulations and (iv) perception of risk of infection. These results point to the importance of influencing social norms as the primary way to improve adherence to the health regulations of COVID-19; more important than intrapsychic considerations such as efficacy and risk. [ABSTRACT FROM AUTHOR]

The Brazilian Labour Force Survey publishes monthly national indicators based on 3‐month rolling data. This paper presents state‐space models to produce state‐level single‐month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS‐COV‐2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state‐space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation. [ABSTRACT FROM AUTHOR]

Incomplete selection makes it challenging to infer selection on genes at short time scales, especially for microorganisms, due to stronger linkage between loci. However, in many cases, the selective force changes with environment, time, or other factors, and it is of great interest to understand selective forces at this level to answer relevant biological questions. We developed a new method that uses the change in dN / dS , instead of the absolute value of dN / dS , to infer the dominating selective force based on sequence data across geographical scales. If a gene was under positive selection, dN / dS was expected to increase through time, whereas if a gene was under negative selection, dN / dS was expected to decrease through time. Assuming that the migration rate decreased and the divergence time between samples increased from between-continent, within-continent different-country, to within-country level, dN / dS of a gene dominated by positive selection was expected to increase with increasing geographical scales, and the opposite trend was expected in the case of negative selection. Motivated by the McDonald–Kreitman (MK) test, we developed a pairwise MK test to assess the statistical significance of detected trends in dN / dS. Application of the method to a global sample of dengue virus genomes identified multiple significant signatures of selection in both the structural and non-structural proteins. Because this method does not require allele frequency estimates and uses synonymous mutations for comparison, it is less prone to sampling error, providing a way to infer selection forces within species using publicly available genomic data from locations over broad geographical scales. [ABSTRACT FROM AUTHOR]

In the search for life in the Universe, exoplanets represent numerous natural experiments in planet formation, evolution, and the emergence of life. This raises the fascinating prospect of evaluating cosmic life on a statistical basis. One key statistic is the occurrence rate of life-bearing worlds, f L, the 'frequency of life' term in the famous Drake Equation. Measuring f L would give profound insight into how common life is and may help us to constrain origin-of-life theories. I propose f L as the goal for the DRAKE mission (Dedicated Research for Advancing Knowledge of Exobiology): a transit spectroscopy survey of M-dwarf habitable zone terrestrial planets. I investigate how the uncertainty on the observed value of f L scales with sample size. I determine that sampling error dominates over observational error and that the uncertainty is a function of the observed f L value. I show that even small sample sizes can provide significant constraints on f L, boding well for the transit spectroscopy approach. I perform a feasibility study of the DRAKE mission using a nominal instrument design and mission plan. Due to low observing efficiencies, DRAKE may need to be incorporated into a wider-ranging deep-space or lunar observatory. A 50-planet survey could constrain f L to ≤ 0.06 (at 95 per cent confidence) if the sample f L  = 0, or 0.03–0.2 if the sample f L  = 0.1. This can be achieved (on average) in 10 yr using a 17-m telescope with an unrestricted field-of-regard. DRAKE is a viable approach to attempting the first experimental measurement of f L. [ABSTRACT FROM AUTHOR]

Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper statistical inference, that is, research on properly quantifying the uncertainty of the (noisy) sample estimate regarding the true value in the population, is currently still limited. This article proposes and evaluates several strategies to compute valid differentially private confidence intervals for the median. Instead of computing a differentially private point estimate and deriving its uncertainty, we directly estimate the interval bounds and discuss why this approach is superior if ensuring privacy is important. We also illustrate that addressing both sources of uncertainty—the error from sampling and the error from protecting the output—simultaneously should be preferred over simpler approaches that incorporate the uncertainty in a sequential fashion. We evaluate the performance of the different algorithms under various parameter settings in extensive simulation studies and demonstrate how the findings could be applied in practical settings using data from the 1940 Decennial Census. [ABSTRACT FROM AUTHOR]

Secondary contact between species often results in the formation of a hybrid zone, with the eventual fates of the hybridizing species dependent on evolutionary and ecological forces. We examine this process in the Amazon Basin by conducting the first genomic and phenotypic characterization of the hybrid zone formed after secondary contact between two obligate army‐ant‐followers: the White‐breasted Antbird (Rhegmatorhina hoffmannsi) and the Harlequin Antbird (Rhegmatorhina berlepschi). We found a major geographic displacement (∼120 km) between the mitochondrial and nuclear clines, and we explore potential hypotheses for the displacement, including sampling error, genetic drift, and asymmetric cytonuclear incompatibilities. We cannot exclude roles for sampling error and genetic drift in contributing to the discordance; however, the data suggest expansion and unidirectional introgression of hoffmannsi into the distribution of berlepschi. [ABSTRACT FROM AUTHOR]

Secondary contact between species often results in the formation of a hybrid zone, with the eventual fates of the hybridizing species dependent on evolutionary and ecological forces. We examine this process in the Amazon Basin by conducting the first genomic and phenotypic characterization of the hybrid zone formed after secondary contact between two obligate army‐ant‐followers: the White‐breasted Antbird (Rhegmatorhina hoffmannsi) and the Harlequin Antbird (Rhegmatorhina berlepschi). We found a major geographic displacement (∼120 km) between the mitochondrial and nuclear clines, and we explore potential hypotheses for the displacement, including sampling error, genetic drift, and asymmetric cytonuclear incompatibilities. We cannot exclude roles for sampling error and genetic drift in contributing to the discordance; however, the data suggest expansion and unidirectional introgression of hoffmannsi into the distribution of berlepschi. [ABSTRACT FROM AUTHOR]

The adoption of a multivariate perspective of selection implies the existence of multivariate adaptive peaks and pervasive correlational selection that promotes co‐adaptation between traits. However, to test for the ubiquity of correlational selection in nature, we must first have a sense of how well can we estimate multivariate nonlinear selection (i.e., the γ‐matrix) in the face of sampling error. To explore the sampling properties of estimated γ‐matrices, we simulated inidividual traits and fitness under a wide range of sample sizes, using different strengths of correlational selection and of stabilizing selection, combined with different number of traits under selection, different amounts of residual variance in fitness, and distinct patterns of selection. We then ran nonlinear regressions with these simulated datasets to simulate γ‐matrices after adding random error to individual fitness. To test how well could we detect the imposed pattern of correlational selection at different sample sizes, we measured the similarity between simulated and imposed γ‐matrices. We show that detection of the pattern of correlational selection is highly dependent on the total strength of selection on traits and on the amount of residual variance in fitness. Minimum sample size needs to be at least 500 to precisely estimate the pattern of correlational selection. Furthermore, a pattern of selection in which different sets of traits contribute to different functions is the easiest to diagnose, even when using a large number of traits (10 traits), but with sample sizes in the order of 1000 individuals. Consequently, we recommend working with sets of traits from distinct functional complexes and fitness proxies less prone to effects of environmental and demographic stochasticity to test for correlational selection with lower sample sizes. [ABSTRACT FROM AUTHOR]

Background: Vitamin A test results have historically been notorious for poor repeatability and reproducibility. This problem has been discussed at length in Association of American Feed Control Officials Laboratory Methods and Services Committee meetings. Objective: The objective of this work was to assess the effect of test portion mass on the repeatability of vitamin A test results. Methods: The study was conducted in two parts. In Part I, fundamental sampling error (FSE) was determined experimentally through replicated (n = 16) vitamin A testing of three animal feed materials. The testing followed rigorous test portion selection for 10 g and 100 g test portions. In Part II, FSE calculations were made (1) using theoretical equations based on vitamin A as a liberated analyte and (2) on representing the particles in feed materials. Particle size characterization of vitamin A ingredients was estimated by microscopy and further evaluated by particle size analysis. Results: RSDs, % for vitamin A determinations ranged from 10.5-24.7, and 2.26-10.7 for 10 g and 100 g test portions, respectively. FSE calculated for Ingredient A ranged from 18.3-101% and 5.79-32.0% for 10 g and 100 g test portions, respectively, and for Ingredient B, ranged from 10.2-56.2% and 3.21-17.8% for 10 g and 100 g test portions, respectively. Conclusion: Test portion mass has a substantial impact on FSE and is an important factor in controlling the random error in vitamin A testing. FSE equations are useful to approximate minimum test portion mass. Highlights: Vitamin A method development should use theoretical predictions and experimental verification to guide test portion mass. Strategies to deal with the larger test portion masses will be key to validating new methods. [ABSTRACT FROM AUTHOR]

We introduce Bilby-MCMC , a Markov chain Monte Carlo sampling algorithm tuned for the analysis of gravitational waves from merging compact objects. Bilby-MCMC provides a parallel-tempered ensemble Metropolis-Hastings sampler with access to a block-updating proposal library including problem-specific and machine learning proposals. We demonstrate that learning proposals can produce over a 10-fold improvement in efficiency by reducing the autocorrelation time. Using a variety of standard and problem-specific tests, we validate the ability of the Bilby-MCMC sampler to produce independent posterior samples and estimate the Bayesian evidence. Compared to the widely used Dynesty nested sampling algorithm, Bilby-MCMC is less efficient in producing independent posterior samples and less accurate in its estimation of the evidence. However, we find that posterior samples drawn from the Bilby-MCMC sampler are more robust: never failing to pass our validation tests. Meanwhile, the Dynesty sampler fails the difficult-to-sample Rosenbrock likelihood test, over constraining the posterior. For CBC problems, this highlights the importance of cross-sampler comparisons to ensure results are robust to sampling error. Finally, Bilby-MCMC can be embarrassingly and asynchronously parallelized making it highly suitable for reducing the analysis wall-time using a High Throughput Computing environment. Bilby-MCMC may be a useful tool for the rapid and robust analysis of gravitational-wave signals during the advanced detector era and we expect it to have utility throughout astrophysics. [ABSTRACT FROM AUTHOR]

Hair analysis has been mainly used to document drug use history in abusers, drug-facilitated crime cases, doping control analysis and postmortem toxicology in the fields of forensic toxicology, clinical toxicology, and doping control. Hair analysis has also gained more attention in the last 30 years in China. Relevant technology has been promoted as more research has appeared concerning hair analysis, and consensus has been sought among forensic toxicologists regarding aspects such as hair decontamination treatment, detection of abused substances in hair, segmental hair analysis and interpretation of analytical results. However, there are still some limitations in the estimation of drug intake time and frequency by segmental hair analysis due to the different growth cycles evident within a bundle of hairs, the drug incorporation mechanism and sampling errors. Microsampling and imaging mass spectrometry (IMS) technology based on a single hair may be a good choice to estimate drug intake time more accurately. Analysis of hair root samples may also be used to document acute poisoning in postmortem toxicology, and the analysis of the hair shaft can document long-term use of drugs depending on the length of the hair being evaluated. [ABSTRACT FROM AUTHOR]

Nested sampling is an important tool for conducting Bayesian analysis in Astronomy and other fields, both for sampling complicated posterior distributions for parameter inference, and for computing marginal likelihoods for model comparison. One technical obstacle to using nested sampling in practice is the requirement (for most common implementations) that prior distributions be provided in the form of transformations from the unit hyper-cube to the target prior density. For many applications – particularly when using the posterior from one experiment as the prior for another – such a transformation is not readily available. In this letter, we show that parametric bijectors trained on samples from a desired prior density provide a general purpose method for constructing transformations from the uniform base density to a target prior, enabling the practical use of nested sampling under arbitrary priors. We demonstrate the use of trained bijectors in conjunction with nested sampling on a number of examples from cosmology. [ABSTRACT FROM AUTHOR]

In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors or sampling bias. Without explicit adjustment, machine learning algorithms trained using these data can produce out‐of‐sample predictions which propagate these undesirable correlations. We propose a method to pre‐process the training data, producing an adjusted dataset that is statistically independent of the nuisance variables with minimum information loss. We develop a conceptually simple approach for creating an adjusted dataset in high‐dimensional settings based on a constrained form of matrix decomposition. The resulting dataset can then be used in any predictive algorithm with the guarantee that predictions will be statistically independent of the nuisance variables. We develop a scalable algorithm for implementing the method, along with theory support in the form of independence guarantees and optimality. The method is illustrated on some simulation examples and applied to two case studies: removing machine‐specific correlations from brain scan data, and removing ethnicity information from a dataset used to predict recidivism. That the motivation for removing undesirable correlations is quite different in the two applications illustrates the broad applicability of our approach. [ABSTRACT FROM AUTHOR]

Background: Pesticide residues are analyzed in thousands of samples yearly by national authorities and private laboratories. Intensive research is ongoing to develop new methods or improve existing ones concentrating on extraction, cleanup, and detection techniques. Little attention has been paid to the contribution of prior steps in the determination process to overall laboratory sampling errors, though several publications demonstrate their practical importance. Consequently, the repeatability and reproducibility of the results are often reported based on the recovery tests alone. A few previous publications are cited in this paper which illustrate the magnitude of random errors derived from subsampling, comminution of analytical samples, and selection of small test portions. Objectives: We aim to call attention to the importance of considering all steps of laboratory sampling and analysis processes in calculating the combined uncertainty of results and realistic performance assessments of methods including their longterm intermediate precision. Method: Validation of laboratory sampling of large fruits is used to illustrate the recommended procedures, determination of their random error, and long-term method performance. Results: The results indicate that subsampling, comminution, and selection of test portions can be major contributors to the combined uncertainty of results. Conclusions: All these steps should be considered in estimation of random variation (uncertainty) of measured residues. Highlights: Random error of laboratory sampling for pesticide residues. Mass reduction of large crop units. Internal quality control of laboratory operations. [ABSTRACT FROM AUTHOR]

The National Marine Fisheries Service is the agency charged with estimating the number of fish removed from US oceans by recreational anglers. Two surveys produce data for these estimates; one measures number of angler trips and the other fish caught per trip by species, geography, and time period. Both surveys collect data from people and have the usual sources of nonsampling error afflicting demographic surveys. Due to lack of accessibility to fishing sites, they also have additional sources such as undercoverage. The rare incidence of saltwater fishing among the general population makes the current method costly and time-consuming. Consequently, new ways of obtaining information to supplement or replace the current method are of interest. One is the electronic logbook (ELB); this approach allows anglers to self-report their catch using cellphones or other communication devices. Estimation of catch from these data are possible with estimators using capture-recapture methods, but new sources of nonsampling error are rising. In this article, we examine three sources of nonsampling error in estimators of catch and approximate their biasing effect. We illustrate the method by comparing the effects using data from an ELB study in the Gulf of Mexico. [ABSTRACT FROM AUTHOR]

Motivation High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. Results In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. Availability and implementation The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Sampling error during screening and surveillance endoscopy is a well-recognized problem. Wide-area transepithelial sampling with three-dimensional computer-assisted analysis (WATS3D), used adjunctively to forceps biopsy (FB), has been shown to increase the detection of Barrett's esophagus (BE) and BE-associated neoplasia. We evaluated the clinical utility of WATS3D and its impact on the management of patients with BE and dysplasia. Between 2013 and 2018, 432 consecutive patients who had a WATS3D positive and an accompanying FB negative result were identified. Physicians were contacted to determine if the WATS3D result impacted their decision to enroll patients in surveillance or increase the frequency of surveillance, recommend ablation, and/or initiate or increase the dose of proton pump inhibitors (PPIs). WATS3D directly impacted the management of 97.8% of 317 BE patients; 96.2% were enrolled in surveillance and 60.2% were started on PPIs or their dose was increased. WATS3D impacted the management of 94.9% and 94.1% of the 98 low-grade dysplasia and 17 high-grade dysplasia patients, respectively. As a result of WATS3D, 33.7% of low-grade dysplasia and 70.6% of high-grade dysplasia patients underwent endoscopic therapy. More than 37% of all dysplasia patients were enrolled in a surveillance program, and nearly 30% were scheduled to be surveilled more frequently. PPIs were either initiated, or the dose was increased in more than 54% of all dysplasia patients. We demonstrate that WATS3D has high clinical utility. By prompting physicians to change their clinical management in patients with negative FB results, WATS3D, used adjunctively to FB, directly impacts patient management, and improves patient outcomes. [ABSTRACT FROM AUTHOR]

Cosmological analyses of galaxy surveys rely on knowledge of the redshift distribution of their galaxy sample. This is usually derived from a spectroscopic and/or many-band photometric calibrator survey of a small patch of sky. The uncertainties in the redshift distribution of the calibrator sample include a contribution from shot noise, or Poisson sampling errors, but, given the small volume they probe, they are dominated by sample variance introduced by large-scale structures. Redshift uncertainties have been shown to constitute one of the leading contributions to systematic uncertainties in cosmological inferences from weak lensing and galaxy clustering, and hence they must be propagated through the analyses. In this work, we study the effects of sample variance on small-area redshift surveys, from theory to simulations to the COSMOS2015 data set. We present a three-step Dirichlet method of resampling a given survey-based redshift calibration distribution to enable the propagation of both shot noise and sample variance uncertainties. The method can accommodate different levels of prior confidence on different redshift sources. This method can be applied to any calibration sample with known redshifts and phenotypes (i.e. cells in a self-organizing map , or some other way of discretizing photometric space), and provides a simple way of propagating prior redshift uncertainties into cosmological analyses. As a worked example, we apply the full scheme to the COSMOS2015 data set, for which we also present a new, principled SOM algorithm designed to handle noisy photometric data. We make available a catalogue of the resulting resamplings of the COSMOS2015 galaxies. [ABSTRACT FROM AUTHOR]

In the current era of Gaia and large, high signal-to-noise stellar spectroscopic surveys, there is an unmet need for a reliable library of fundamentally calibrated stellar effective temperatures based on accurate stellar diameters. Here, we present a set of precision diameters and temperatures for a sample of 6 dwarf, 5 sub-giant, and 5 giant stars observed with the PIONIER beam combiner at the VLTI. Science targets were observed in at least two sequences with five unique calibration stars each for accurate visibility calibration and to reduce the impact of bad calibrators. We use the standard PIONIER data reduction pipeline, but bootstrap over interferograms, in addition to employing a Monte Carlo approach to account for correlated errors by sampling stellar parameters, limb darkening coefficients, and fluxes, as well as predicted calibrator angular diameters. The resulting diameters were then combined with bolometric fluxes derived from broad-band Hipparcos–Tycho photometry and MARCS model bolometric corrections, plus parallaxes from Gaia to produce effective temperatures, physical radii, and luminosities for each star observed. Our stars have mean angular diameter and temperatures uncertainties of 0.8 per cent and 0.9 per cent, respectively, with our sample including diameters for 10 stars with no pre-existing interferometric measurements. The remaining stars are consistent with previous measurements, with the exception of a single star which we observe here with PIONIER at both higher resolution and greater sensitivity than was achieved in earlier work. [ABSTRACT FROM AUTHOR]

This essay attempts to describe the apples and oranges problem in meta-analyses. Essentially, some meta-analyses combine original studies of various variables that are not the same pairs of variables. Metaphorically, they meta-analyze the effects of fruit when they should conduct separate meta-analyses of apples and oranges. This practice is inconsistent with the assumptions behind the meta-analytic formulae concerning sampling error and makes meta-analytic estimates difficult to interpret. Meta-analysis teams are advised to justify their choices and types of evidence are described to assist researchers and reviewers in assessing and justifying when constructs can and cannot be combined together in a meta-analysis. [ABSTRACT FROM AUTHOR]

In the analysis of survey data, it is of interest to estimate and quantify uncertainty about means or totals for each of several nonoverlapping subpopulations or areas. When the sample size for a given area is small, standard confidence intervals based on data only from that area can be unacceptably wide. In order to reduce interval width, practitioners often utilize multilevel models in order to borrow information across areas, resulting in intervals centered around shrinkage estimators. However, such intervals only have the nominal coverage rate on average across areas under the assumed model for across-area heterogeneity. The coverage rate for a given area depends on the actual value of the area mean and can be nearly zero for areas with means that are far from the across-group average. As such, the use of uncertainty intervals centered around shrinkage estimators are inappropriate when area-specific coverage rates are desired. In this article, we propose an alternative confidence interval procedure for area means and totals under normally distributed sampling errors. This procedure not only has constant 1 − α frequentist coverage for all values of the target quantity but also uses auxiliary information to borrow information across areas. Because of this, the corresponding intervals have shorter expected lengths than standard confidence intervals centered on the unbiased direct estimator. Importantly, the coverage of the procedure does not depend on the assumed model for across-area heterogeneity. Rather, improvements to the model for across-area heterogeneity result in reduced expected interval width. [ABSTRACT FROM AUTHOR]

Summary: We consider integrating a non‐probability sample with a probability sample which provides high dimensional representative covariate information of the target population. We propose a two‐step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded concave penalties to select important variables and show selection consistency for general samples. In the second step, we focus on a doubly robust estimator of the finite population mean and re‐estimate the nuisance model parameters by minimizing the asymptotic squared bias of the doubly robust estimator. This estimating strategy mitigates the possible first‐step selection error and renders the doubly robust estimator root n consistent if either the sampling probability or the outcome model is correctly specified. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was to review and illustrate the sometimes diagnostically challenging features of cardiac sarcoidosis. We emphasize variable phenotypes presented at explant and biopsy evaluation and review literature regarding ancillary clinical and pathologic studies to enhance diagnostic accuracy.Methods: A literature review was performed and two cardiac sarcoidosis cases were illustrated.Results: Our cases and literature review demonstrate the pathologic spectrum of cardiac sarcoidosis. Irregular left ventricular free wall involvement is most common, followed by the interventricular septum and right ventricle. Although granulomas are often composed of tight epithelioid macrophage aggregates, early granulomas comprise loosely associated macrophages with lymphocyte predominance. Chronic disease leads to fibrosis and end-stage heart failure. Sampling errors and variable histology cause low endomyocardial biopsy sensitivity.Conclusions: Current guidelines use clinical, radiologic, and immunohistologic criteria for diagnosing cardiac sarcoidosis. Knowledge of these guidelines will assist pathologists in making accurate diagnosis of this disease. [ABSTRACT FROM AUTHOR]

We examine empirically the generalizability of internally valid micro-estimates of causal effects in a fixed population over time when that population is subject to aggregate shocks. Using panel data, we show that the returns to investments in agriculture in India and Ghana, small and medium non-farm enterprises in Sri Lanka, and schooling in Indonesia fluctuate significantly across time periods. We show how the returns to these investments interact with specific, measurable, and economically relevant aggregate shocks, focusing on rainfall and price fluctuations. We also obtain lower-bound estimates of confidence intervals of the returns based on estimates of the parameters of the distributions of rainfall shocks in our two agricultural samples. We find that even these lower-bound confidence intervals are substantially wider than those based solely on sampling error that are commonly provided in studies, most of which are based on single-year samples. We also find that cross-sectional variation in rainfall cannot be confidently used to replicate within-population rainfall variability. Based on our findings, we discuss methods for incorporating information on external shocks into evaluations of the returns to policy. [ABSTRACT FROM AUTHOR]

The objective of this research was to develop a sampling protocol for contaminated soils after a large radiological accident. One of the criteria for good sampling method is reproducibility and accuracy of large number of samples collected in short time. Members of the chemical laboratories of the Fire Rescue Service of the Czech Republic (FRS CR), which are included in Radiation Monitoring Network, tested four tools in different soil types. More than 100 samples were collected. Root auger and square soil sampler seem to be the best for reliable soil sampling. Modifications of the root auger decreased of relative error of sampling to 7.5%. The root auger is recommended as a first-choice sampling tool based on: (1) ease, (2) operator safety, (3) collection of a representative sample (4) applicability to most soil types and (5) enabling collection of depth-discrete samples. [ABSTRACT FROM AUTHOR]

Linked data are increasingly being used for epidemiological research, to enhance primary research, and in planning, monitoring and evaluating public policy and services. Linkage error (missed links between records that relate to the same person or false links between unrelated records) can manifest in many ways: as missing data, measurement error and misclassification, unrepresentative sampling, or as a special combination of these that is specific to analysis of linked data: the merging and splitting of people that can occur when two hospital admission records are counted as one person admitted twice if linked and two people admitted once if not. Through these mechanisms, linkage error can ultimately lead to information bias and selection bias; so identifying relevant mechanisms is key in quantitative bias analysis. In this article we introduce five key concepts and a study classification system for identifying which mechanisms are relevant to any given analysis. We provide examples and discuss options for estimating parameters for bias analysis. This conceptual framework provides the 'links' between linkage error, information bias and selection bias, and lays the groundwork for quantitative bias analysis for linkage error. [ABSTRACT FROM AUTHOR]

Spectral induced polarization (SIP), describing the measurement of the frequency domain electrical impedance magnitude and phase of porous materials, has been widely used to characterize subsurface hydrological/biogeochemical properties and processes. SIP data collected at frequencies higher than 100 Hz are expected to describe the polarization of small particles providing insights into the physicochemical properties of clays, nanoparticles and microorganisms. However, the phase measurements at these high frequencies are often contaminated by errors due to the parasitic capacitive coupling of the SIP instrument, especially for lower conductivity samples. We developed a model showing the measured phase is the sum of the true sample phase and an error term ωC in Z x, where ω is the angular frequency; C in is the instrument input capacitance and Z x is a measurable impedance function related to the sample holder properties and the reference resistor. Based on this model, a new phase correction method is proposed that results in highly accurate SIP data up to 20 kHz as well as the determination of C in. We tested the correction method using electric circuits, NaCl fluids and three unconsolidated samples (sand, sand-clay and sand-pyrite mixtures). The corrected phase for the circuit and NaCl fluid experiments showed excellent agreement with the theoretical phase response across the studied frequency range (errors <1 mrad). For unconsolidated samples, removal of errors results in phase spectra more consistent with expected polarization mechanisms, as based on phase peaks recorded for small pyrite and clay particles at high frequencies. These phase peaks could not be identified in the uncorrected data. Our approach can substantially enhance the value of the SIP method for the characterization of fine-grained sediments and rocks. [ABSTRACT FROM AUTHOR]

Motivation Predictive models are a powerful tool for solving complex problems in computational biology. They are typically designed to predict or classify data coming from the same unknown distribution as the training data. In many real-world settings, however, uncontrolled biological or technical factors can lead to a distribution mismatch between datasets acquired at different times, causing model performance to deteriorate on new data. A common additional obstacle in computational biology is scarce data with many more features than samples. To address these problems, we propose a method for unsupervised domain adaptation that is based on a weighted elastic net. The key idea of our approach is to compare dependencies between inputs in training and test data and to increase the cost of differently behaving features in the elastic net regularization term. In doing so, we encourage the model to assign a higher importance to features that are robust and behave similarly across domains. Results We evaluate our method both on simulated data with varying degrees of distribution mismatch and on real data, considering the problem of age prediction based on DNA methylation data across multiple tissues. Compared with a non-adaptive standard model, our approach substantially reduces errors on samples with a mismatched distribution. On real data, we achieve far lower errors on cerebellum samples, a tissue which is not part of the training data and poorly predicted by standard models. Our results demonstrate that unsupervised domain adaptation is possible for applications in computational biology, even with many more features than samples. Availability and implementation Source code is available at https://github.com/PfeiferLabTue/wenda. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

We introduce a new approach for locating earthquakes using arrival times derived from waveforms. The most costly computational step of the algorithm scales as the number of stations in the active seismographic network. In this approach, a variation on existing grid search methods, a series of full waveform simulations are conducted for all receiver locations, with sources positioned successively at each station. The traveltime field over the region of interest is calculated by applying a phase picking algorithm to the numerical wavefields produced from each simulation. An event is located by subtracting the stored traveltime field from the arrival time at each station. This provides a shifted and time-reversed traveltime field for each station. The shifted and time-reversed fields all approach the origin time of the event at the source location. The mean or median value at the source location thus approximates the event origin time. Measures of dispersion about this mean or median time at each grid point, such as the sample standard error and the average deviation, are minimized at the correct source position. Uncertainty in the event position is provided by the contours of standard error defined over the grid. An application of this technique to a synthetic data set indicates that the approach provides stable locations even when the traveltimes are contaminated by additive random noise containing a significant number of outliers and velocity model errors. It is found that the waveform-based method out-performs one based upon the eikonal equation for a velocity model with rapid spatial variations in properties due to layering. A comparison with conventional location algorithms in both a laboratory and field setting demonstrates that the technique performs at least as well as existing techniques. [ABSTRACT FROM AUTHOR]

Directed acyclic graphs are widely used to describe directional pairwise relations. Such relations are estimated by reconstructing a directed acyclic graph's structure, which is challenging when the ordering of nodes of the graph is unknown. In such a situation, existing methods such as the neighbourhood and search-and-score methods have high estimation errors or computational complexities, especially when a local or sequential approach is used to enumerate edge directions by testing or optimizing a criterion locally, as a local method may break down even for moderately sized graphs. We propose a novel approach to simultaneously identifying all estimable directed edges and model parameters, using constrained maximum likelihood with nonconvex constraints. We develop a constraint reduction method that constructs a set of active constraints from super-exponentially many constraints. This, coupled with an alternating direction method of multipliers and a difference convex method, permits efficient computation for large-graph learning. We show that the proposed method consistently reconstructs identifiable directions of the true graph and achieves the optimal performance in terms of parameter estimation. Numerically, the method compares favourably with competitors. A protein network is analysed to demonstrate that the proposed method can make a difference in identifying the network's structure. [ABSTRACT FROM AUTHOR]

Summary: In high dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received much attention recently. To provide a proper amount of shrinkage, it is typical to use a grid search and a model comparison criterion to find the optimal regularization parameters. However, we show that fixing the parameters across all folds may result in an inconsistency issue, and it is more appropriate to cross‐validate projection–selection patterns to obtain the best coefficient estimate. Our in‐sample error studies in jointly sparse and rank deficient models lead to a new class of information criteria with four scale‐free forms to bypass the estimation of the noise level. By use of an identity, we propose a novel scale‐free calibration to help cross‐validation to achieve the minimax optimal error rate non‐asymptotically. Experiments support the efficacy of the methods proposed. [ABSTRACT FROM AUTHOR]

Multipower estimators, widespread for their robustness to the presence of jumps, are also useful for reducing the estimation error of integrated volatility powers even in the absence of jumps. Optimizing linear combinations of multipowers can indeed drastically reduce the variance with respect to traditional estimators. In the case of quarticity, we also prove that the optimal combination is a nearly efficient estimator, being arbitrarily close to the nonparametric efficiency bound as the number of consecutive returns employed diverges. We provide guidance on how to select the optimal number of consecutive returns to minimize mean square error. The implementation on U.S. stock prices corroborates our theoretical findings and further shows that our proposed quarticity estimator noticeably reduces the number of detected jumps, and improves the quality of volatility forecasts. [ABSTRACT FROM AUTHOR]

The article offers a reply to a response by James G. Webster and Harsh Taneja to the authors' issue article "Networks of Audience Overlap in the Consumption of Digital News." Topics include the use of network methods to analyze audience data, the use of statistical modeling in relation to sampling error, and the implications of audience overlap for cultural aspects of mass media audiences.

The branching order of major angiosperm lineages is a challenging phylogenetic question that has received substantial attention in recent years. Two main competing hypotheses place the New Caledonian Amborella as either sister to all other extant angiosperms (Amborella-sister) or to thewater lilies (AmborellaþNymphaeales). Here, we revisit this question by expanding a transcriptomic data set of 310 genes previously assembled to include data from seven species comprising two major lineages of flowering plants that were poorly represented or missing from the original study. We also applied gene genealogy interrogation, a recent approach based on constrained tree searches in combination with topology tests, to account for gene tree estimation error and its downstream effects in coalescent analyses. In addition to gene genealogy interrogation, we conducted a large number of multilocus analyses, including concatenation and coalescent approaches (using both unconstrained and constrained gene trees), and based on different data sets (original and expanded) and data types (nucleotide and amino acid sequences). We show that the majority of gene trees favor Amborella-sister topology, and all multilocus analyses conducted (concatenation and coalescent) provide overwhelming support for this hypothesis regardless of data type. Beyond resolving the evolutionary root of angiosperms with confidence, our results highlight the importance of both broadening taxonomic sampling in phylogenomics and addressing the effects of gene tree error in summary coalescent inferences. [ABSTRACT FROM AUTHOR]

BACKGROUND Carotid-femoral pulse wave velocity (cfPWV) is the most established measure of central arterial stiffness and is calculated by dividing the distance travelled by the pulse wave by the pulse transit time. However, there is no universally accepted standardized measurement of pulse travel distance for cfPWV. This study sought to assess validity and convertibility of 2 most frequently used travel distance estimations, and create the simple and useful conversion equation to unify cfPWV values obtained with different methodologies for pulse travel distance. METHODS In a total of 227 adults, cfPWV was calculated using 2 different pulse travel distances: suprasternum-femoral distance minus suprasternum-carotid distance (the subtraction method) and carotid-femoral straight distance × 0.8 (the 80% method). They were compared against 3D arterial tracing via magnetic resonance imaging (MRI). RESULTS The subtraction method underestimated travel distance and cfPWV by 8.7% although correlations with the MRI reference values were significant. The 80% method provided more reliable cfPWV, showing a stronger linearity (r = 0.96, P < 0.0001) and a better agreement with the MRI-based reference value (+0.02±0.54 m/s). Values of cfPWV were influenced primarily by pulse transit time, explaining ~80% of the variation in cfPWV, and the contribution of pulse travel distance was relatively small irrespective of how the travel distance was measured. After the application of the conversion factor (the 80% method = the subtraction method × 1.1), cfPWV values obtained with both methods were strongly correlated and estimation errors were comparable (+0.03±0.75 m/s). CONCLUSION Our findings indicate that the subtraction method and the 80% method can provide equivalent cfPWV values by the application of a simple conversion factor. [ABSTRACT FROM AUTHOR]

Location awareness has become one of the most popular areas for mobile internet applications. Normally, the method of maximum likelihood estimation (MLE) can be used to estimate the position of the unknown nodes. However, when the number of anchor nodes is small within the communication range, the traditional methods cannot work very well. This paper provides a novel location approach to estimate the position by selecting valuable neighbors (both anchor and regular nodes) with Minimized Error Propagation Location method based on Error estimation (MEPLE). The simulation results show that the MEPLE outperforms the MLE overall, especially when the number of anchor nodes is relatively small such as between 10 and 20, MEPLE can reduce 50-80% estimation error on average. [ABSTRACT FROM AUTHOR]

Aims Vegetation sampling employing observers is prone to both interobserver and intra-observer error. Three types of errors are common: (i) overlooking error (i.e. not observing species actually present), (ii) misidentification error (i.e. not correctly identifying species) and (iii) estimation error (i.e. not accurately estimating abundance). I conducted a literature review of 59 articles that provided quantitative estimates or statistical inferences regarding observer error in vegetation studies. Important Findings Almost all studies (92%) that tested for a statistically significant effect of observer error found at least one significant comparison. In surveys of species composition, mean pseudoturnover (the percentage of species overlooked by one observer but not another) was 10-30%. Species misidentification rates were on the order of 5-10%. The mean coefficient of variation (CV) among observers in surveys of vegetation cover was often several hundred % for species with low cover, although CVs of 25-50% were more representative of species with mean covers of >50%. A variety of metrics and indices (including commonly used diversity indices) and multivariate data analysis techniques (including ordinations and classifications) were found to be sensitive to observer error. Sources of error commonly include both characteristics of the vegetation (e.g. small size of populations, rarity, morphology, phenology) and attributes of the observers (e.g. mental fatigue, personal biases, differences in experience, physical stress). The use of multiple observers, additional training including active feedback approaches, and continual evaluation and calibration among observers are recommended as strategies to reduce observer error in vegetation surveys. [ABSTRACT FROM AUTHOR]

Although random routes are an important face-to-face sampling method, there is very little research about the procedure. The idea that random route sampling leads to a uniform selection of households has never been tested. This study challenges this notion in a simulation and uses registration office data to verify the impact of selection errors on survey results. All tested routes strongly violate the equal probability assumption and lead to biased expected values in multiple variables. The strongest errors were found in variables that were related to the spatial location of households. Furthermore, errors were related not only to random route instructions but also to the selection of starting points for these routes. This study is of relevance for scientists using random route data for their research as well as for surveys applying random route procedures. Data analysts can use the results to gain an impression about the degree of bias, whereas researchers that use random route samples should view these findings as inducement to improve the random route methodology and develop alternatives. [ABSTRACT FROM AUTHOR]