Sample size calculations: basic principles and common pitfalls

Sample size determination is often an important step in planning a statistical study-and it is usually a dif cult one. Among the important hurdles to be surpassed, one must obtain an estimate of one or more error variances and specify an effect size of importance. There is the temptation to take some shortcuts. This article offers some suggestions for successful and meaningful sample size determination. Also discussed is the possibility that sample size may not be the main issue, that the real goal is to design a high-quality study. Finally, criticism is made of some ill-advised shortcuts relating to power and sample size.

2011

The sample size is the number of patients or other experimental units that need to be included in a study to answer the research question. Pre-study calculation of the sample size is important; if a sample size is too small, one will not be able to detect an effect, while a sample that is too large may be a waste of time and money. Methods to calculate the sample size are explained in statistical textbooks, but because there are many different formulas available, it can be difficult for investigators to decide which method to use. Moreover, these calculations are prone to errors, because small changes in the selected parameters can lead to large differences in the sample size. This paper explains the basic principles of sample size calculations and demonstrates how to perform such a calculation for a simple study design.

Critical Care, 2002

The present review introduces the notion of statistical power and the hazard of under-powered studies. The problem of how to calculate an ideal sample size is also discussed within the context of factors that affect power, and specific methods for the calculation of sample size are presented for two common scenarios, along with extensions to the simplest case.

Medical science, 2014

It is mandatory to calculate sample size in any type of research in medical science to get a generalized conclusion for the population under study. If the study is well designed with a desired sample size then the standard error will be less and the power and precision will be good. All statistical procedures become valid in this context.

International Journal of Statistics in Medical Research, 2017

Determining the optimal sample size is crucial for any scientific investigation. An optimal sample size provides adequate power to detect statistical significant difference between the comparison groups in a study and allows the researcher to control for the risk of reporting a false-negative finding (Type II error). A study with too large a sample is harder to conduct, expensive, time consuming and may expose an unnecessarily large number of subjects to potentially harmful or futile interventions. On the other hand, if the sample size is too small, a best conducted study may fail to answer a research question due to lack of sufficient power. To draw a valid and accurate conclusion, an appropriate sample size must be determined prior to start of any study. This paper covers the essentials in calculating sample size for some common study designs. Formulae along with some worked examples were demonstrated for potential applied health researchers. Although maximum power is desirable, this is not always possible given the resources available for a study. Researchers often needs to choose a sample size that makes a balance between what is desirable and what is feasible.

Journal of Ayub Medical College, Abbottabad : JAMC

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.

AYU (An International Quarterly Journal of Research in Ayurveda), 2014

of view. Today, statistics is an indispensable tool in each and every field of health science research, whether it is Medicine, Ayurveda, Pharmacy, Dental or other allied health sciences. Statistics helps even clinicians in extracting vital information from the empirical data that ultimately lead to improved patient care. Statistical concepts are required to be considered throughout a study, from planning to the final reporting stage. This article provides a brief overview of statistical methods used at various stages of a research study with the main emphasis on estimation of minimum sample size for various types of objectives. Role of Statistics in Research Studies The first step in any research study is to clearly state aims of the study followed by its objectives, which are focused at achieving the aim. Many use the terms aim and objective interchangeably, whereas the two terms have their own meaning with clear-cut distinction. Aim of the study is a general statement about main and broad study question, meaning that it is not measurable. On the other hand, objectives of a research study should be (Specific, Measureable, Achievable, Relevant and Time-bound) and limited in number. Before stating the objectives, the researcher should know about the types of variables and/or attributes being assessed in the study, especially the exposure and outcome measurements and distinction between the two in the analytical studies. This is required in order to ensure the characteristics of the objectives stated above. The second step

Journal of advanced …, 2004

How many do I need? Basic principles of sample size estimation Background. In conducting randomized trials, formal estimations of sample size are required to ensure that the probability of missing an important difference is small, to reduce unnecessary cost and to reduce wastage. Nevertheless, this aspect of research design often causes confusion for the novice researcher. Aim. This paper attempts to demystify the process of sample size estimation by explaining some of the basic concepts and issues to consider in determining appropriate sample sizes. Method. Using a hypothetical two group, randomized trial as an example, we examine each of the basic issues that require consideration in estimating appropriate sample sizes. Issues discussed include: the ethics of randomized trials, the randomized trial, the null hypothesis, effect size, probability, significance level and type I error, and power and type II error. The paper concludes with examples of sample size estimations with varying effect size, power and alpha levels.

This paper is designed as a tool that a researcher could use in planning and conducting quality research. This is a review paper which gives a discussion of various aspects of designing consideration in medical research. This paper covers the essentials in calculating power and sample size for a variety of applied study designs. Sample size computation for survey type of studies, observation studies and experimental studies based on means and proportions or rates, sensitivityspecificity tests for assessing the categorical outcome are presented in detail. Over the last decades, considerable interest has been focused on medical research designs and sample size estimation. The resulting literature is scattered over many textbooks and journals. This paper presents these methods in a single review and comments on their application in practice.

Indian Journal of Anaesthesia, 2016

Addressing a sample size is a practical issue that has to be solved during planning and designing stage of the study. The aim of any clinical research is to detect the actual difference between two groups (power) and to provide an estimate of the difference with a reasonable accuracy (precision). Hence, researchers should do a priori estimate of sample size well ahead, before conducting the study. Post hoc sample size computation is not encouraged conventionally. Adequate sample size minimizes the random error or in other words, lessens something happening by chance. Too small a sample may fail to answer the research question and can be of questionable validity or provide an imprecise answer while too large a sample may answer the question but is resource-intensive and also may be unethical. More transparency in the calculation of sample size is required so that it can be justified and replicated while reporting.