Dr. Helen Malone – Visiting Research Fellow (Nursing) Trinity College, Dublin. Dr. Honor Nicholl – Assistant Professor (Nursing) Trinity College, Dublin. Professor Imelda Coyne – Professor (Nursing) Trinity College, Dublin. How can service users contribute to healthcare research? How Good is your Estimated Sample Size? Getting the Essential Parameters into Focus This poster presentation is aimed at the novice nursing and midwifery researcher. Findings from healthcare research have the potential to be translated into clinical practice. As such, sample size determination warrants adequate time and attention in the planning stage of a research study. Too small a sample size may have insufficient power to statistically detect a specified meaningful effect. Too large a sample size may be statistically unnecessary, and unethical. The aim of this literature review is to highlight the importance of inputting appropriate parameter values into sample size calculations or software programs. A review of the literature included data sources CINAHL, Google Scholar and Statistical texts Four parameter values required for basic sample size estimation are: Significance level, Power, Effect size and Standard deviation. (1)Conventional Significance level is typically 5%. (2)Conventional Power chosen is typically 80%. (3)Effect size is the clinical meaningful difference which can justify a change in clinical practice i.e. a clinically important difference. (4)Standard deviation is usually unknown and can be estimated by utilising the standard deviation from previously analogous studies or a representative and appropriately sized pilot study. Background : Data Sources Key Findings Aims and Objectives The first three parameters are chosen by the researcher. Consequently sample size estimation should be considered to be an estimate only. Increasing Significance level, (i.e. 10% to 5%), power (i.e. 80% to 90%) and Standard deviation increases sample size. Decreasing Significance level, Power and Standard deviation decreases the estimated sample size. In contrast, increasing Effect size decreases the estimated sample size and increasing the Effect size decreases the estimated sample size. A small adjustment of Effect size can result in large changes in estimated sample size. G*Power where appropriate allows for the input of either the standardised or the absolute effect size. Absolute effect size for a mean is │µ1 - µ2│ Absolute effect size for a proportion is │p1 - p2│ The absolute effect size changes to the standardised effect size when divided by the standard deviation i.e. │µ 1 – µ2 / σ for means and │p1 - p2│/ σ for proportions. Carefully estimated sample size estimates are important because: The research informing Nursing and Midwifery practice needs to be evidenced-based. Underpinning the concept of evidence-based practice is the application of research findings that are statistically sound. Further information from: Conclusions