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Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of nonsampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each anses as the Bureau of Economic Analysis penodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with nonresponse, and the Bureau of Labor Statistics seasonally adjusts employment statistics. I anchor my discussion of communication of uncertainty in the contribution of Oskar Morgenstern (1963a), who argued forcefully for agency publication of error estimates for official economic statistics.

Evaluating and validating satellite sea surface salinity (SSS) measurements is fundamental. There are two types of errors in satellite SSS: measurement error due to the instrument’s inaccuracy and problems in retrieval, and sampling error due to unrepresentativeness in the way that the sea surface is sampled in time and space by the instrument. In this study, we focus on sampling errors, which impact both satellite and in situ products. We estimate the sampling errors of Level 3 satellite SSS products from Aquarius, SMOS and SMAP, and in situ gridded products. To do that, we use simulated L2 and L3 Aquarius, SMAP and SMOS SSS data, individual Argo observations and gridded Argo products derived from a 12-month high-resolution 1/48° ocean model. The use of the simulated data allows us to quantify the sampling error and eliminate the measurement error. We found that the sampling errors are high in regions of high SSS variability and are globally about 0.02/0.03 psu at weekly time scales and 0.01/0.02 psu at monthly time scales for satellite products. The in situ-based product sampling error is significantly higher than that of the three satellite products at monthly scales (0.085 psu) indicating the need to be cautious when using in situ-based gridded products to validate satellite products. Similar results are found using a Correlated Triple Collocation method that quantifies the standard deviation of products’ errors acquired with different instruments. By improving our understanding and quantifying the effect of sampling errors on satellite-in situ SSS consistency over various spatial and temporal scales, this study will help to improve the validation of SSS, the robustness of scientific applications and the design of future salinity missions.

Ocular microbiology deals with miniscule samples from ocular infections, which are difficult to collect, process, and analyze, necessitating special skills, and the knowledge of troubleshooting errors to reach a specific diagnosis. In this article, we highlight several practical points in ocular microbiology, common mistakes, and various approaches to resolve them. We have covered sample collection from different ocular compartments, processing for smear preparation and culture, transport of samples, staining and reagents issues, artifacts and contaminants, and interpretation of in-vitro antimicrobial susceptibility testing reports. This review aims to help both ophthalmologists and microbiologists in making the practice of ocular microbiology and the interpretation of reports more reliable, hassle-free, and precise.

This study addresses the challenge of estimating variances in household surveys, particularly when sampling design variables are absent in publicly available microdata. By implementing the Generalized Variance Function (GVF), Colombia's Household Survey for 2022 serves as a case study. GVF models were developed and validated using the standard errors published by the National Administrative Department of Statistics (DANE) of Colombia. These models demonstrated high accuracy and robustness for estimates across various levels of disaggregation and periodicities. Additionally, their validation with 2023 data confirmed their predictive capacity and applicability in similar contexts, underscoring their effectiveness as tools for evaluating the quality of estimates in complex surveys. Este estudio aborda el desafío de estimar la varianza en encuestas de hogares, causado por la ausencia de variables del diseño muestral en los microdatos públicos. A través de la implementación de la Función Generalizada de Varianza (FGV), se utiliza como caso de estudio la Encuesta de Hogares de Colombia para 2022. Los modelos de FGV se desarrollaron y validaron con base en los errores estándar publicados por el Departamento Administrativo Nacional de Estadística (DANE) de Colombia. Estos modelos demostraron alta precisión y robustez en estimaciones a diferentes niveles de desagregación y periodicidades. Asimismo, su validación con datos de 2023 confirmó su capacidad predictiva y aplicabilidad en contextos similares, destacando su eficacia como herramienta para evaluar la calidad de las estimaciones en encuestas complejas.

The internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.

Taylor's law, one of the most widely accepted generalizations in ecology, states that the variance of a population abundance time series scales as a power law of its mean. Here we reexamine this law and the empirical evidence presented in support of it. Specifically, we show that the exponent generally depends on the length of the time series, and its value reflects the combined effect of many underlying mechanisms. Moreover, sampling errors alone, when presented on a double logarithmic scale, are sufficient to produce an apparent power law. This raises questions regarding the usefulness of Taylor's law for understanding ecological processes. As an alternative approach, we focus on short-term fluctuations and derive a generic null model for the variance-to-mean ratio in population time series from a demographic model that incorporates the combined effects of demographic and environmental stochasticity. After comparing the predictions of the proposed null model with the fluctuations observed in empirical data sets, we suggest an alternative expression for fluctuation scaling in population time series. Analyzing population fluctuations as we have proposed here may provide new applied (e.g., estimation of species persistence times) and theoretical (e.g., the neutral theory of biodiversity) insights that can be derived from more generally available short-term monitoring data.

We report results of an experiment designed to assess whether the payment of contingent incentives to respondents in Karnataka, India, impacts the quality of survey data. Of 2276 households sampled at the city block level, 934 were randomly assigned to receive a small one-time payment at the time of the survey, whereas the remaining households did not receive this incentive. We analyse the effects of incentives across a range of questions that are common in survey research in less developed countries. Our study suggests that incentives reduced unit non-response. Conditionally on participation, we also find little impact of incentives on a broad range of sociodemographic, behavioural and attitudinal questions. In contrast, we consistently find that households that received incentives reported substantially lower consumption and income levels and fewer assets. Given random assignment and very high response rates, the most plausible interpretation of this finding is that incentivizing respondents in this setting may increase their motivation to present themselves as more needy, whether to justify the current payment or to increase the chance of receiving resources in the future. Therefore, despite early indications that contingent incentives may raise response rates, the net effect on data quality must be carefully considered.

Monsoon rainfall is central to the climate of West Africa, and understanding its variability is a challenge for which satellite rainfall products could bewell suited to contribute to. Their quality in this region has received less attention than elsewhere. The focus is set on the scales associated with atmospheric variability, and a meteorological benchmark is set up with ground-based observations from the African Monsoon Multidisciplinary Analysis (AMMA) program. The investigation is performed at various scales of accumulation using four gauge networks. The seasonal cycle is analyzed using 10-day-averaged products, the synoptic-scale variability is analyzed using daily means, and the diurnal cycle of rainfall is analyzed at the seasonal scale using a composite and at the diurnal scale using 3-hourly accumulations. A novel methodology is introduced that accounts for the errors associated with the areal–time rainfall averages. The errors from both satellite and ground rainfall data are computed using dedicated techniques that come down to an estimation of the sampling errors associated to these measurements. The results show that the new generation of combined infrared–microwave (IR–MW) satellite products is describing the rain variability similarly to ground measurements. At the 10-day scale, all products reveal high regional and seasonal skills. The day-to-day comparison indicates that some products perform better than others, whereas all of them exhibit high skills when the spectral band of African easterly waves is considered. The seasonal variability of the diurnal scale as well as its relative daily importance is only captured by some products. Plans for future extensive intercomparison exercises are briefly discussed.

This paper describes the compensation method for average current sampling error under the operating condition of low sampling-to-fundamental frequency ratio. When the sampling-to-fundamental frequency ratio is lowered, the current ripple is very large, and an error between the sampled current and the average current during one sampling period occurs. The actual average current can be obtained from the relationship between the average voltage and current of the synchronous reference frame. The actual average voltage and reference voltage match when the inverter output voltage has no error. Thus, the proposed compensation method of average current sampling error can be implemented based on the reference voltage and sampled current. The proposed current compensation method is verified by simulations and experiments.

To improve mitigation planning on railways, it is crucial to accurately assess the number of fatalities resulting from this type of infrastructure. To illustrate the relevance of considering the two main errors from the sampling process (searcher efficiency and carcass persistence), we corrected the estimation of mammal fatalities (> 1 kg) on a 750-km railway located in the Brazilian Savanna accounting for these errors. Observed fatalities were collected by two observers in a rail inspection vehicle (RV). We estimated searcher efficiency comparing the number of carcasses found during the searcher efficiency trials on foot and by RV on the same subsections of the railway. Carcass persistence was estimated based on trials with a subset of carcasses that had their persistence verified over three consecutive days. Searcher efficiency was 29.8% and daily carcass persistence probability was 99.7%. Nine surveys on the entire railway (2015–2016) recorded 1950 carcasses and, after correcting for the sampling errors, we estimated that 4286 mammals died on this railway in 23 months. This estimate is 2.2 times larger than the observed number of fatalities. These errors should not be neglected, as our results demonstrate that the number of observed carcasses is a misperception in relation to the true number of fatalities caused by the railway. However, our results also indicate that dealing with these errors in railway ecology is still a challenge. We stress the need to test more suitable sampling designs for fatality estimates on railways.