Average Run Length Performance of theuChart with Control Limits Based on the Average Sample Size

Methodological reporting guidelines for studies of event-related potentials (ERPs) were updated in Psychophysiology in 2014. These guidelines facilitate the communication of key methodological parameters (e.g., preprocessing steps). Failing to report key parameters represents a barrier to replication efforts, and difficultly with replicability increases in the presence of small sample sizes and low statistical power. We assessed whether guidelines are followed and estimated the average sample size and power in recent research. Reporting behavior, sample sizes, and statistical designs were coded for 150 randomly-sampled articles from five high-impact journals that frequently publish ERP research from 2011 to 2017. An average of 63% of guidelines were reported, and reporting behavior was similar across journals, suggesting that gaps in reporting is a shortcoming of the field rather than any specific journal. Publication of the guidelines paper had no impact on reporting behavior, suggesting that editors and peer reviewers are not enforcing these recommendations. The average sample size per group was 21. Statistical power was conservatively estimated as .72-.98 for a large effect size, .35-.73 for a medium effect, and .10-.18 for a small effect. These findings indicate that failing to report key guidelines is ubiquitous and that ERP studies are primarily powered to detect large effects. Such low power and insufficient following of reporting guidelines represent substantial barriers to replication efforts. The methodological transparency and replicability of studies can be improved by the open sharing of processing code and experimental tasks and by a priori sample size calculations to ensure adequately powered studies.

Download Full-text

On the Estimation Error in Zero-Inflated Poisson Model for Process Control

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539303001068 ◽

2003 ◽

Vol 10 (02) ◽

pp. 159-169 ◽

Cited By ~ 11

Author(s):

B. He ◽

M. Xie ◽

T. N. Goh ◽

P. Ranjan

Keyword(s):

Sample Size ◽

Poisson Distribution ◽

Control Chart ◽

Average Run Length ◽

Estimation Error ◽

Accurate Estimate ◽

Minimum Size ◽

High Quality ◽

Run Length ◽

Run Length Distribution

The control chart based on a Poisson distribution has often been used to monitor the number of defects in sampling units. However, many false alarms could be observed due to extra zero counts, especially for high-quality processes. Therefore, some alternatives have been developed to alleviate this problem, one of which is the control chart based on the zero-inflated Poisson distribution. This distribution takes into account the extra zeros present in the data, and yield more accurate results than the Poisson distribution. However, implementing a control chart is often based on the assumption that the parameters are either known or an accurate estimate is available. For a high quality process, an accurate estimate may require a very large sample size, which is seldom available. In this paper the effect of estimation error is investigated. An analytical approximation is derived to compute shift detection probability and run length distribution. The study shows that the false alarm rates are higher than the desirable level for smaller values of the sample size. This is further supported by smaller average run length. In general, the quantitative results from this paper can be utilized to select a minimum size of the initial sample for estimating the control limits so that certain average run length requirements are met.

Download Full-text

Precision Analysis of Poisson Control Chart Based on Sample Size

Jurnal Matematika Statistika dan Komputasi ◽

10.20956/jmsk.v16i3.6944 ◽

2020 ◽

Vol 16 (3) ◽

pp. 325

Author(s):

Elsa Resa Sari

Keyword(s):

Quality Control ◽

Sample Size ◽

Control Chart ◽

Average Run Length ◽

Statistical Quality Control ◽

Sample Sizes ◽

Precision Analysis ◽

Run Length ◽

Statistical Quality ◽

Result Show

One technique used in performing statistical quality control is by poisson control chart. Poisson control chart used in data that have the same mean and varians for monitoring the number of defects in the study. In some cases, the different sample sizes influence the control chart performance. The control chart performance can be measured using average run length (ARL). The smaller ARL’s value, the better type of control chart. In this study, we used different sample sizes that is and mean . The result show the best performance of control chart is when and m = 200, because its has a smaller ARL’s value.

Download Full-text

Variable sample size EWMA CV chart based on expected average run length

10.1063/1.5121124 ◽

2019 ◽

Author(s):

Anis Nabila Binti Muhammad ◽

Chong Zhi Lin ◽

Yeong Wai Chung ◽

Lam Weng Siew

Keyword(s):

Sample Size ◽

Average Run Length ◽

Run Length ◽

Variable Sample Size

Download Full-text

EXPRESS: The influence of sample size on preferences from experience

Quarterly Journal of Experimental Psychology ◽

10.1177/17470218211044013 ◽

2021 ◽

pp. 174702182110440

Author(s):

Janine Hoffart ◽

Jana Jarecki ◽

Gilles Dutilh ◽

Jörg Rieskamp

Keyword(s):

Sample Size ◽

Cognitive Modeling ◽

Learning Strategy ◽

Small Samples ◽

Selling Price ◽

Individual Level ◽

Average Sample Size ◽

Sample Information ◽

How People Learn ◽

Average Sample

People often learn from experience about the distribution of outcomes of risky options. Typically, people draw small samples, when they can actively sample information from risky gambles to make decisions. We examine how the size of the sample that people experience in decision from experience affects their preferences between risky options. In two studies (N=40 each) we manipulated the size of samples that people could experience from risky gambles and measured subjective selling prices and the confidence in selling price judgments after sampling. The results show that, on average, sample size influenced neither the selling prices nor confidence. However, cognitive modeling of individual-level learning showed that most participants could be classified as Bayesian learners, whereas the minority adhered to a frequentist learning strategy and that if learning was cognitively simpler more participants adhered to the latter. The observed selling prices of Bayesian learners changed with sample size as predicted by Bayesian principles, whereas sample size affected the judgments of frequentist learners much less. These results illustrate the variability in how people learn from sampled information and provide an explanation for why sample size often does not affect judgments.

Download Full-text

Lower Bounds for Average Sample Size and Efficiency of Statistical Inference Procedures

Theory of Probability and Its Applications ◽

10.1137/1124009 ◽

1979 ◽

Vol 24 (1) ◽

pp. 120-129 ◽

Cited By ~ 8

Author(s):

I. N. Volodin

Keyword(s):

Sample Size ◽

Statistical Inference ◽

Lower Bounds ◽

Average Sample Size ◽

Average Sample

Download Full-text

Evaluation of an np control chart under truncated life test using quick switching sampling system

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217721314 ◽

2017 ◽

Vol 40 (12) ◽

pp. 3407-3414 ◽

Cited By ~ 7

Author(s):

P Jeyadurga ◽

S Balamurali

Keyword(s):

Control Chart ◽

Average Run Length ◽

Optimal Parameters ◽

Life Test ◽

Sampling System ◽

Run Length ◽

Truncated Life Test ◽

Control Limits

In this paper, we propose a new attribute np control chart for monitoring Weibull distributed mean life of a product using a quick switching sampling system. The proposed control chart consists of two pairs of control limits, namely normal and tightened control limits. The optimal parameters of the proposed control chart, such as coefficients of control limits and experiment termination ratio, are determined so that the average run length (ARL) is close to the target in-control ARL. The ARL is calculated for various shift constants for the corresponding determined parameters. The performance of the proposed control chart is evaluated and compared with other existing charts in terms of ARL.

Download Full-text

Sequential Testing in Reliability and Validity Studies With Repeated Measurements per Subject

International Journal of Statistics and Probability ◽

10.5539/ijsp.v8n1p120 ◽

2018 ◽

Vol 8 (1) ◽

pp. 120

Author(s):

Steven B. Kim ◽

Jeffrey O. Wand

Keyword(s):

Sample Size ◽

High Reliability ◽

Reliability And Validity ◽

Repeated Measurements ◽

Sequential Test ◽

Ratio Test ◽

Systematic Bias ◽

Average Sample Size ◽

Validity Studies ◽

Average Sample

In medical, health, and sports sciences, researchers desire a device with high reliability and validity. This article focuses on reliability and validity studies with n subjects and m ≥2 repeated measurements per subject. High statistical power can be achieved by increasing n or m, and increasing m is often easier than increasing n in practice unless m is too high to result in systematic bias. The sequential probability ratio test (SPRT) is a useful statistical method which can conclude a null hypothesis H0 or an alternative hypothesis H1 with 50% of the required sample size of a non-sequential test on average. The traditional SPRT requires the likelihood function for each observed random variable, and it can be a practical burden for evaluating the likelihood ratio after each observation of a subject. Instead, m observed random variables per subject can be transformed into a test statistic which has a known sampling distribution under H0 and under H1. This allows us to formulate a SPRT based on a sequence of test statistics. In this article, three types of study are considered: reliabilityof a device, reliability of a device relative to a criterion device, and validity of a device relative to a  criterion device. Using SPRT for testing the reliability of a device, for small m, results in an average sample size of about 50% of the fixed sample size for a non-sequential test. For comparing a device to criterion, the average sample size approaches to 60% approximately as m increases. The SPRT tolerates violation of normality assumption for validity study, but it does not for reliability study.

Download Full-text