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The aim of this paper is to propose alternative sampling schemes for indoor air quality assessments in which a balance between the sampling time required and number of sampling points chosen is allowed.

CO₂ was used as a reference to evaluate the existing and the proposed sampling schemes for average indoor pollutant concentration regarding the sampling time and sampling point density required and the probable errors induced at certain confidence levels of the measurement.

The results showed that increasing the number of sampling points would significantly reduce the corresponding sampling time required: at the same confidence level, doubling the sampling point density from 250 m² to 500 m² per measurement point would reduce the sampling time required from eight hours to three hours.

The model parameters were determined from surveys for some typical offices in Hong Kong and therefore may require adaptation for use elsewhere.

A useful source of information in determining the number of sampling points chosen and the duration of the measurement needed according to the “acceptable” measurement uncertainty allowed.

Providing flexibility in IAQ assessment, the results would be useful to policymakers for making decisions on resources and manpower management for their respective sampling strategies while taking the uncertainties into account.

The purpose of this paper is to investigate a new sampling methodology previously proposed for systems with a known lifetime distribution: the Predetermined Sampling Intervals (PSI) method.

The methodology is defined on basis of system hazard cumulative rate, and is compared with other approaches, particularly those whose parameters may change in real time, taking into account current sample information.

For different lifetime distributions, the results obtained for adjusted average time to signal (AATS) using a control chart for the sample mean are presented and analysed. They demonstrate the high degree of statistical performance of this sampling procedure, particularly when used in systems with an increasing failure rate distribution.

This PSI method is important from a quality and reliability management point of view.

This methodology involves a process by which sampling instants are obtained at the beginning of the process to be controlled. Also this new approach allows for statistical comparison with other sampling schemes, which is a novel feature.

The purpose of this paper is to examine commonly relied upon product sampling strategies, direct‐to‐consumer sampling and event sampling, to determine which method can deliver the greatest return on investment in a variety of situations.

The paper explores information and data collected via case studies in actual programs presented to brands. The trial, sample, and purchase numbers were actual averages from sampling effectiveness studies for these types of programs.

The paper identifies and segments different types of products and the method by which the products are most effectively implemented into trial and sampling programs. ICOM reveals hard statistics on the return on investment of programs utilizing multiple methods of sampling including point‐of‐use, direct mail and event sampling.

Marketers should follow the STEPS outlined in this study to apply the best brand sampling strategy for a given product. Knowledge of targeted consumer base along with careful pre‐event analysis will deliver the best return on investment for a trial campaign.

The paper reveals reasons for a growing shift in corporate budgeted marketing dollars from event marketing to direct consumer product sampling. While event sampling is not always an ineffective or inferior marketing method,the reader can discover methods for a pre‐event return on investment analysis that will reveal the sampling strategy sure to deliver the most “bang” for the marketing “buck”.

The most frequently used attribute sampling plan in MIL‐STD 105 D. In cases, however, when the quality level of incoming lots is generally sufficiently good, MIL‐STD 105 D often leads to unnecessarily high sampling cost. This can be avoided by using α‐optimal sampling plans. The authors outline the α‐optimal sampling scheme along with a simple procedure to determine α‐optimal sampling plans at workshop level. These plans depend on three parameters which have to be estimated from recorded data. In this article the effects of estimation errors in these parameters are investigated.

The commitment to statistical process control programmes is becoming commonplace in US industry. However, some companies are experiencing failure of these programmes, particularly in multi‐strata (population) production processes. Even if such a process is in a state of statistical control, there is a high likelihood that one or more strata could drift away from the target owing to an assignable cause. The success of a QC programme depends on the ability of a quality control practitioner to detect this shift with a greater statistical power (sensitivity) and take corrective actions. Addresses the problem faced by the multi‐strata production process of a local manufacturing company in detecting a single stratum shift from the target with a high level of sensitivity. Proposes the selection of an appropriate sampling method (stratified or random) to have a strong bearing on the relative sensitivity of detecting the above shift in a single stratum. Develops power curves for the above mentioned process under stratified and random sampling scenarios, when a shift occurs in a single stratum. Examines the relationship of sample size to the threshold level of the stratum shift and the preferred sampling method.

The purpose of this paper is to provide a rigorous, statistically correct, and low‐cost way to audit sample a lender's loan portfolio, be they a microlender or other type of lender. No other paper applies this method to loan portfolios, even though it is a high demand application.

Standard techniques of audit sampling and dollar unit sampling with stratification are applied to the particular case of a microlender's portfolio. Unlike the audit sampling that almost all auditors use, no arbitrary rules of thumb are applied.

The paper finds that statistical audit sampling for a lender's loan portfolio is simple, rigorous, and inexpensive.

In audit sampling, most auditors use arbitrary rules of thumb and have no idea whether they are sampling enough items to actually be sure, with some desired level of confidence, that they have found no defects. This simple, inexpensive, and statistically rigorous technique will allow auditors who actually want to do a good job to quantify the precision of their statements in a very common application.

This paper combines several disparate threads from the statistical literature on audit sampling in a way that auditors (who are usually not statisticians) can apply them for auditing the quality of a lender's portfolio – microfinance or otherwise – which is a very common need.

The red bead experiment (RBE) is very well‐known in total quality management. It was designed by Dr W.E. Deming to show managers several lessons in management and sampling. The RBE has evolved over several decades and now has an accepted standard format. The RBE also serves as an introduction to control charts which are an important tool in statistical quality control. Surprisingly very little is known about the statistical properties of the RBE. This is most likely due to the fact that the RBE involves mechanical sampling and so is much more complicated than probability sampling. New methods are given for statistical analysis when mechanical sampling is involved (e.g. mechanical sampling of non‐uniform output in a manufacturing environment). This approach has specific applications to control charts which are a major tool in quality control. In a rigorous analysis, it is shown that p, the sample proportion of red beads in the RBE, is an unbiased estimator of the RBE process defect rate. Expressions are also found for the variance of p, enabling the calculation of control chart limits. Some of the techniques used can be applied to other mechanical sampling situations which may at first viewing seem intractable.

This study investigates the repetitive mixed sampling (MRS) plan based on the Cpk index that was proposed by Aslam et al. (2013a). They were the first to study the MRS plan, but they did not pay attention to the fact that submitting to the variable inspection a sample that was first submitted to the attribute inspection, truncates the X observations. In addition, they did not work with an accurate expression to calculate the probabilities of the Cpk statistic.

The authors presented the results based on their original sampling plan through Monte Carlo simulation and defined the theoretical results of their plan when the sample submitted to the variable inspection is no longer the same one submitted to the attribute inspection.

The β risks of the optimum sampling plans presented by Aslam et al. (2013a) are pretty high, exceeding 46%, on average – this same problem was also observed in Saminathan and Mahalingam (2018), Balamurali (2020) and Balamurali et al. (2020), where the β risks of their proposed sampling plans are yet higher.

In terms of originality, the authors can declare the following. It is not a big deal to propose new sampling plans, if one does not know how to obtain their properties. The miscalculations of the sampling plans risks are dangerous; imagine the situation where the acceptance of bad lots exceeds 50% just because the sampling plan was incorrectly designed. Yes, it is a big deal to warn that this type of problem is arising in a growing number of papers. The authors of this study are the pioneers to discover that many studies focusing on the sampling plans need to be urgently revised.

This purpose of this paper is to present an optimization‐based approach to support the design of attribute sampling plans for lot acceptance purposes, with the fraction of non‐conforming items being modeled by a Poisson probability distribution function.

The paper approach stands upon the minimization of the error of the probability of acceptance equalities in the controlled points of the operating curve (OC) with respect to sample size and acceptance number. It was applied to simple and double sampling plans, including several combinations of quality levels required by the producer and the consumer. Formulation of the design of acceptance sampling plans as an optimization problem, having as a goal the minimization of the squared error at the controlled points of the OC curve, and its subsequent solution employing GAMS.

The results are in strong agreement with acceptance sampling plans available in the open literature. The papers approach in some scenarios outperforms classical sampling plans and allows one to identify the lack of feasible solutions.

An optimization‐based approach to support the design of acceptance sampling plans for attributes was conceived and tested. It allows for a general treatment of these problems, including the identification of a lack of feasible solutions, as well as making possible the determination of feasible alternatives by relaxing some model constraints.

Examines the trade‐off between system redundancy and acceptance sampling as alternative means to improve system reliability. We assume that components of the system follow an exponential failure law and investigate expected times to failure of systems of various levels of component redundancy which have been exposed to acceptance sampling plans of various levels of stringency. We also show the probability distributions of system reliability for outgoing lots at various levels of component redundancy and sampling stringency. The paper shows a straightforward method for calculating these trade‐offs and provides the decision maker with a previously unavailable tool of system design and testing.