21 papers · ranked by Valyu relevance
Konstantina Vasileiou, Julie Barnett, Susan Thorpe, Terry Young
Background Choosing a suitable sample size in qualitative research is an area of conceptual debate and practical uncertainty. That sample size principles, guidelines and tools have been developed to enable researchers to set, and justify the acceptability of, their sample size is an indication that the issue…
Ying Cao, Ronald C Chen, Aaron J Katz
A sample comprises the individuals from whom we collect data and represents a share of the population (N) for whom we want to draw conclusions (eg, women breast cancer). The sample size (n) is the number of individual people, experimental units, or other elements included in a sample, and is a central concept in…
D. Samuel Schwarzkopf, Zien Huang
Most studies in psychology, neuroscience, and life science research make inferences about how strong an effect is on average in the population. Yet, many research questions could instead be answered by testing for the universality of the phenomenon under investigation. By using reliable experimental designs that…
Michael W. Beets, Lauren von Klinggraeff, R. Glenn Weaver, Bridget Armstrong + 1 more
'Bridget Armstrong' 'Sarah Burkart'] Background Careful consideration and planning are required to establish “sufficient” evidence to ensure an investment in a larger, more well-powered behavioral intervention trial is worthwhile. In the behavioral sciences, this process typically occurs where smaller-scale studies…
Rik Crutzen, Gjalt-Jorn Y. Peters
A first example of such a situation is when studying a specific subgroup of the population (e.g., patients suffering from a rare disease), it can be very hard to recruit many participants. One could argue that in such cases some evidence is better than none (and some have, Edwards et al., ), but this line of reasoning…
Barun Kumar Nayak
Research is done to find a solution to a particular medical problem (formulated as a research question which in turn is) based on statistics. In an ideal situation, the entire population should be studied but this is almost impossible. Other than census, which is conducted on each and every person of the population…
Ibrahim Salay, Jagath Senarathne
Small sample sizes pose significant challenges in regression analysis, often leading to violations of classical assumptions such as normality, homoscedasticity, and independence of residuals. These violations compromise parameter estimation accuracy, reduce statistical power, and limit the generalizability of findings.…
Maren Hackenberg, Connor, Sophia G., Fabian Kabus + 11 more
The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as how best to include underrepresented groups in data-driven policy and decision…
Christopher Tyler
In the present era of big data analysis, there is a general sense that selective data reporting, ie, cherry-picking, data-peeking, or p-hacking in any form, is wrong and leads to false or misleading conclusions ([7]; [4]). Faced with a large dataset with numerous testable results, it is very tempting to seize on those…
Gordon H Guyatt, Edward J Mills, Diana Elbourne
Background to the debate: Systematic reviews that combine high-quality evidence from several trials are now widely considered to be at the top of the hierarchy of clinical evidence. Given the primacy of systematic reviews-and the fact that individual clinical trials rarely provide definitive answers to a clinical…
Robin J. Boyd, Gary D. Powney, Oliver L. Pescott
In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of "big data", however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of…
Crispin Jordan, Nicola Romanò, John Menzies
All in vivo studies using laboratory animals should be guided by the Three Rs: Replacement, Reduction and Refinement. The concept of Reduction is important in sample size estimation; the sample size used should allow the detection of a biologically meaningful effect size using appropriate statistical tests, but not at…
Authors not listed
Active learning (AL) can significantly accelerate drug discovery by iteratively selecting informative molecules, reducing experimental workload. However, existing AL studies typically assume access to large datasets, an unrealistic scenario for most academic labs. Here, we investigate AL strategies tailored…
Authors not listed
Introduction: Blood microsampling (BµS) devices collect less than 100 µL of blood, offering a less invasive and more cost-effective alternative to venipuncture. However, its metabolomic comparability to conventional samples remains unclear, and standardized BµS metabolomic workflows are lacking. Objectives: This study…
Jörg Martin, Clemens Elster
We consider Bayesian sample size determination using a criterion that utilizes the first two moments of the expected posterior variance. We study the resulting sample size in dependence on the chosen prior and explore the success rate for bounding the posterior variance below a prescribed limit under the true sampling…
Gauri Sankar Datta, Malay Ghosh
The need for small area estimates is increasingly felt in both the public and private sectors in order to formulate their strategic plans. It is now widely recognized that direct small area survey estimates are highly unreliable owing to large standard errors and coefficients of variation. The reason behind this is…
Joseph F. Mudge, Jeffrey E. Houlahan
Traditional study design tools for estimating appropriate sample sizes are not consistently used in ecology and can lead to low statistical power to detect biologically relevant effects. We have developed a new approach to estimating optimal sample sizes, requiring only three parameters; a maximum acceptable average of…
Mohamad Adam Bujang
Determination of a minimum sample size required for a study is a major consideration which all researchers are confronted with at the early stage of developing a research protocol. This is because the researcher will need to have a sound prerequisite knowledge of inferential statistics in order to enable him/her to…
Ilya Novikov
The goal of any estimation study is an interval estimation of a the parameter(s) of interest. These estimations are mostly expressed using empirical confidence intervals that are based on sample point estimates of the corresponding parameter(s). In contrast, calculations of the necessary sample size usually use…
Pamela Reinagel
After an experiment has been completed and analyzed, a trend may be observed that is “not quite significant”. Sometimes in this situation, researchers incrementally grow their sample size N in an effort to achieve statistical significance. This is especially tempting in situations when samples are very costly or…
Charles W. Champ, Andrew V. Sills
A case is made that researchers are interested in studying processes. Often the inferences they are interested in making are about the process and its associated population. On other occasions, a researcher may be interested in making an inference about the collection of individuals the process has generated. We will…