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Behavior Research Methods Jan 2023Recent insights into problems with common statistical practice in psychology have motivated scientists to consider alternatives to the traditional frequentist approach...
Recent insights into problems with common statistical practice in psychology have motivated scientists to consider alternatives to the traditional frequentist approach that compares p-values to a significance criterion. While these alternatives have worthwhile attributes, Francis (Behavior Research Methods, 40, 1524-1538, 2017) showed that many proposed test statistics for the situation of a two-sample t-test are based on precisely the same information in a given data set; and for a given sample size, one can convert from any statistic to the others. Here, we show that the same relationship holds for the equivalent of a one-sample t-test. We derive the relationships and provide an on-line app that performs the computations. A key conclusion of this analysis is that many types of tests are based on the same information, so the choice of which approach to use should reflect the intent of the scientist and the appropriateness of the corresponding inferential framework for that intent.
Topics: Humans; Data Interpretation, Statistical; Research Design; Sample Size; Bayes Theorem
PubMed: 35262898
DOI: 10.3758/s13428-021-01775-3 -
Journal of Ayub Medical College,... 2018One of frequently asked question by medical and dental students / researchers is how to determine the sample size. Sample size calculations is necessary for approval of...
One of frequently asked question by medical and dental students / researchers is how to determine the sample size. Sample size calculations is necessary for approval of research projects, clearance from ethical committees, approval of grant from funding bodies, publication requirement for journals and most important of all justify the authenticity of study results. Determining the sample size for a study is a crucial component. The goal is to include sufficient numbers of subjects so that statistically significant results can be detected. Using too few subjects' will result in wasted time, effort, money; animal lives etc. and may yield statistically inconclusive results. There are numerous situations in which sample size is determined that varies from study to study. This article will focus on the sample size determination for hypothesis testing that involves means, one sample t test, two independent sample t test, paired sample and one-way analysis of variance.
Topics: Data Interpretation, Statistical; Humans; Research Design; Sample Size; Translational Research, Biomedical
PubMed: 29938444
DOI: No ID Found -
ILAR Journal 2002For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to...
For ethical and economic reasons, it is important to design animal experiments well, to analyze the data correctly, and to use the minimum number of animals necessary to achieve the scientific objectives---but not so few as to miss biologically important effects or require unnecessary repetition of experiments. Investigators are urged to consult a statistician at the design stage and are reminded that no experiment should ever be started without a clear idea of how the resulting data are to be analyzed. These guidelines are provided to help biomedical research workers perform their experiments efficiently and analyze their results so that they can extract all useful information from the resulting data. Among the topics discussed are the varying purposes of experiments (e.g., exploratory vs. confirmatory); the experimental unit; the necessity of recording full experimental details (e.g., species, sex, age, microbiological status, strain and source of animals, and husbandry conditions); assigning experimental units to treatments using randomization; other aspects of the experiment (e.g., timing of measurements); using formal experimental designs (e.g., completely randomized and randomized block); estimating the size of the experiment using power and sample size calculations; screening raw data for obvious errors; using the t-test or analysis of variance for parametric analysis; and effective design of graphical data.
Topics: Animal Welfare; Animals; Animals, Laboratory; Data Interpretation, Statistical; Female; Male; Mice; Models, Animal; Rats; Reproducibility of Results; Research Design
PubMed: 12391400
DOI: 10.1093/ilar.43.4.244 -
Korean Journal of Anesthesiology Aug 2019Most parametric tests start with the basic assumption on the distribution of populations. The conditions required to conduct the t-test include the measured values in...
Most parametric tests start with the basic assumption on the distribution of populations. The conditions required to conduct the t-test include the measured values in ratio scale or interval scale, simple random extraction, normal distribution of data, appropriate sample size, and homogeneity of variance. The normality test is a kind of hypothesis test which has Type I and II errors, similar to the other hypothesis tests. It means that the sample size must influence the power of the normality test and its reliability. It is hard to find an established sample size for satisfying the power of the normality test. In the current article, the relationships between normality, power, and sample size were discussed. As the sample size decreased in the normality test, sufficient power was not guaranteed even with the same significance level. In the independent t-test, the change in power according to sample size and sample size ratio between groups was observed. When the sample size of one group was fixed and that of another group increased, power increased to some extent. However, it was not more efficient than increasing the sample sizes of both groups equally. To ensure the power in the normality test, sufficient sample size is required. The power is maximized when the sample size ratio between two groups is 1 : 1.
Topics: Data Interpretation, Statistical; Normal Distribution; Reproducibility of Results; Research Design; Sample Size
PubMed: 30929413
DOI: 10.4097/kja.d.18.00292 -
Drug Intelligence & Clinical Pharmacy Apr 1988
Topics: Research Design; Statistics as Topic
PubMed: 3371197
DOI: 10.1177/106002808802200418 -
Journal of Clinical Psychopharmacology Apr 2009
Topics: Data Interpretation, Statistical; Humans; Models, Statistical; Research Design
PubMed: 19512970
DOI: 10.1097/JCP.0b013e31819d8925 -
Military Medical Research Feb 2020Methodological quality (risk of bias) assessment is an important step before study initiation usage. Therefore, accurately judging study type is the first priority, and... (Review)
Review
Methodological quality (risk of bias) assessment is an important step before study initiation usage. Therefore, accurately judging study type is the first priority, and the choosing proper tool is also important. In this review, we introduced methodological quality assessment tools for randomized controlled trial (including individual and cluster), animal study, non-randomized interventional studies (including follow-up study, controlled before-and-after study, before-after/ pre-post study, uncontrolled longitudinal study, interrupted time series study), cohort study, case-control study, cross-sectional study (including analytical and descriptive), observational case series and case reports, comparative effectiveness research, diagnostic study, health economic evaluation, prediction study (including predictor finding study, prediction model impact study, prognostic prediction model study), qualitative study, outcome measurement instruments (including patient - reported outcome measure development, content validity, structural validity, internal consistency, cross-cultural validity/ measurement invariance, reliability, measurement error, criterion validity, hypotheses testing for construct validity, and responsiveness), systematic review and meta-analysis, and clinical practice guideline. The readers of our review can distinguish the types of medical studies and choose appropriate tools. In one word, comprehensively mastering relevant knowledge and implementing more practices are basic requirements for correctly assessing the methodological quality.
Topics: Animals; Bias; Humans; Psychometrics; Reproducibility of Results; Research; Research Design
PubMed: 32111253
DOI: 10.1186/s40779-020-00238-8 -
Behavior Research Methods Feb 2021When two independent means μ and μ are compared, H : μ = μ, H : μ≠μ, and H : μ > μ are the hypotheses of interest. This paper introduces the R package SSDbain,...
When two independent means μ and μ are compared, H : μ = μ, H : μ≠μ, and H : μ > μ are the hypotheses of interest. This paper introduces the R package SSDbain, which can be used to determine the sample size needed to evaluate these hypotheses using the approximate adjusted fractional Bayes factor (AAFBF) implemented in the R package bain. Both the Bayesian t test and the Bayesian Welch's test are available in this R package. The sample size required will be calculated such that the probability that the Bayes factor is larger than a threshold value is at least η if either the null or alternative hypothesis is true. Using the R package SSDbain and/or the tables provided in this paper, psychological researchers can easily determine the required sample size for their experiments.
Topics: Bayes Theorem; Humans; Probability; Research Design; Sample Size
PubMed: 32632740
DOI: 10.3758/s13428-020-01408-1 -
American Journal of Respiratory and... Dec 2017The American Thoracic Society committee on Proficiency Standards for Pulmonary Function Laboratories has recognized the need for a standardized reporting format for...
BACKGROUND
The American Thoracic Society committee on Proficiency Standards for Pulmonary Function Laboratories has recognized the need for a standardized reporting format for pulmonary function tests. Although prior documents have offered guidance on the reporting of test data, there is considerable variability in how these results are presented to end users, leading to potential confusion and miscommunication.
METHODS
A project task force, consisting of the committee as a whole, was approved to develop a new Technical Standard on reporting pulmonary function test results. Three working groups addressed the presentation format, the reference data supporting interpretation of results, and a system for grading quality of test efforts. Each group reviewed relevant literature and wrote drafts that were merged into the final document.
RESULTS
This document presents a reporting format in test-specific units for spirometry, lung volumes, and diffusing capacity that can be assembled into a report appropriate for a laboratory's practice. Recommended reference sources are updated with data for spirometry and diffusing capacity published since prior documents. A grading system is presented to encourage uniformity in the important function of test quality assessment.
CONCLUSIONS
The committee believes that wide adoption of these formats and their underlying principles by equipment manufacturers and pulmonary function laboratories can improve the interpretation, communication, and understanding of test results.
Topics: Advisory Committees; Humans; Lung; Research Design; Respiratory Function Tests; Societies, Medical; United States
PubMed: 29192835
DOI: 10.1164/rccm.201710-1981ST -
Korean Journal of Anesthesiology Apr 2020Properly set sample size is one of the important factors for scientific and persuasive research. The sample size that can guarantee both clinically significant... (Review)
Review
Properly set sample size is one of the important factors for scientific and persuasive research. The sample size that can guarantee both clinically significant differences and adequate power in the phenomena of interest to the investigator, without causing excessive financial or medical considerations, will always be the object of concern. In this paper, we reviewed the essential factors for sample size calculation. We described the primary endpoints that are the main concern of the study and the basis for calculating sample size, the statistics used to analyze the primary endpoints, type I error and power, the effect size and the rationale. It also included a method of calculating the adjusted sample size considering the dropout rate inevitably occurring during the research. Finally, examples regarding sample size calculation that are appropriately and incorrectly described in the published papers are presented with explanations.
Topics: Biometry; Humans; Patient Dropouts; Research Design; Sample Size
PubMed: 32229812
DOI: 10.4097/kja.19497