This blog explains the importance of having an adequate sample size in a study.
The blog highlights that what is considered to be a ‘large’ or ‘small’ sample size will vary depending on the condition being studied (e.g. a sample of 1000 people might be small if one was studying a common condition, but it might be a very large sample if one was studying a rare condition).
Nonetheless, an adequate sample size is necessary to ensure a study is adequately powered (which reduces the chance of a Type II error; i.e. wrongly concluding that a particular condition or difference between groups is absent when it does actually exist).
Additionally, the blog describes a meta-epidemiological analysis of systematic reviews that suggests that sample size might also influence the effect size identified in a study. It describes how the researchers ranked 93 meta-analyses in terms of sample size and found that studies with a smaller sample size tended to find larger effect sizes. This may be explained by publication bias among the smaller trials (i.e. bias in favouring of publishing those with significant results). Alternatively, or equally, it may be that larger trials have greater heterogeneity that could also explain a smaller effect size. Smaller trials may also be inadequately powered to detect small effect sizes. Whatever the explanation(s), this blog highlights the importance of considering the sample size of a study when interpreting the results. Read the blog
Students 4 Best Evidence (S4BE) is a growing network of students from around the world, from school age to university, who are interested in learning more about evidence-based healthcare (EBH). The network is supported by the UK Cochrane Centre. In addition to the website, the S4BE has a Facebook group and Twitter feed. For more information, read Selena Ryan-Vigs blog which introduces Students 4 Best Evidence.