Submit Enquiry
  1. Home
  2. Data Analysis
  3. Sample size, effect…

Infographic: Sample size, effect size, and statistical power: A guide

INFOGRAPHICS and DOWNLOADABLES New
  • Marisha Fonseca
  • Marisha Fonseca

    An editor at heart and perfectionist by disposition, providing solutions for journals, publishers, and universities in areas like alt-text writing and publication consultancy.

    Oct 3, 2023

Reading time
1 min
Sample size, effect size, and statistical power: A guide

Ever wondered how many participants you need to ensure your findings are solid? How likely is it that the relationship you’ve detected actually exists and isn’t due to chance? Excited to learn how to go beyond p values in hypothesis testing? Look no further! The infographic below demystifies sample size, statistical power, and effect size for biomedical researchers, breaking down these concepts into bite-sized nuggets so that you can design studies that stand up to scrutiny and make a real impact.

An infographic explaining Sample Size, Effect Size, and Statistical Power for Biomedical Researchers Sample Size • Sample size refers to the number of individuals or data points you include in your research study. • It directly affects the reliability of your findings. Statistical Power • Statistical power is the likelihood of detecting a true relationship, effect, or difference if it exists in your data. • You need adequate power if you want to avoid false-positive and false-negative errors. Effect Size • Effect size tells you whether the differences or relationships that you detect are meaningful in real life. • Larger sample sizes are needed to detect smaller effects accurately. • Biomedical researchers often focus on numerically small differences (e.g., normal vs prediabetes HbA1c levels), so sample size calculations are crucial. Benefits of an Adequate Sample and Statistical Power Representativeness: • A larger sample size can better represent the diversity within the population you're studying. • This reduces the chance of drawing incorrect conclusions based on a biased sample. Precision and Confidence: • A larger sample size leads to more precise estimates and narrower confidence intervals. • This means you can have more confidence in the accuracy of your results. Efficient Use of Resources: • Adequate power means that your findings are likely to be high-quality. • You don’t waste resources on an underpowered study that yields unreliable results. • You don’t waste resources on an unnecessarily large sample size, inconveniencing participants.

An introduction to sample size effect size and statistical power for biomedical researchers.png

Download

Found this useful?

If so, share it with your fellow researchers

Related post

Powerful ways to tackle missing data

May 14, 2024
Data Analysis Data Storage & Management

How to analyze count data in research

November 14, 2023
Data Analysis Data Storage & Management

What principal investigators really need to know...

October 18, 2023

Related Reading

Full disclosure: Editage Insights is a product of Editage, a global provider of world-class scientific communication solutions. Editage Insights is funded by Editage and endorses services provided by Editage but is editorially independent. English Editing - Editage.com | 英文校正 – Editage.jp | 원어민영문교정 – Editage.co.kr | SCI英文论文发表 – Editage.cn | publicação de artigos – Editage.com.br | 編輯英文 – Editage.com.tw