Scientific Method Steps for Experiments

A team once told me their experiment was legit because the sample size was huge. They were wrong and here's why 👇 It’s a common misunderstanding in experimentation. Yes, sample size matters. But it’s only one part of the statistical power equation. You also need to consider: → Effect size → Metric variance Ignore those, and even a massive sample won’t save you. I’ve seen teams using binary metrics with base rates under 0.1%, like 0.05%. In those cases, you’d need over 124 million samples to detect a 2% uplift with appropriate power. I’ve also seen teams give up on smaller samples too early, when in reality the uplift they were testing for was well within reach if the experiment was designed well. Because effect size isn’t just a guess, it’s a design choice. It depends on: → How cleanly your metric reflects your change → How broadly your treatment applies → How directly it changes behaviour The takeaway? Sample size matters... but it’s a small part of a much more complex equation. If you want better experiments: ❌ Don’t just ask “Do we have enough traffic?” ✅ Ask: “Are we designing to generate useful signal?” → What’s the most common experiment myth you’ve had to debunk?

📊 Sample Size Calculation: Made Simple One of the most overlooked (and most critical) steps in any research study is getting the sample size right. Too small ➝ results lack precision and credibility Too large ➝ wasted time, resources, and ethics concerns This infographic breaks down how to calculate sample size for a prevalence study using a simple, widely accepted formula, plus the key adjustments reviewers always look for. 🔍 What it covers: ✔️ Why sample size matters ✔️ The standard formula for prevalence studies ✔️ A clear worked example ✔️ Adjustments for non-response and cluster sampling ✔️ Practical tips when prevalence is unknown Whether you’re a: 👩🏽⚕️ clinician interpreting evidence 📚 student designing your first study 📊 public health officer planning surveys 🧪 researcher preparing a methods section …this is a must-know foundation for credible, ethical, and publishable research. 👉 Save, share, or pass it on to someone working on a proposal right now. #ResearchMethods #Epidemiology #SampleSize #PublicHealth #Biostatistics #StudyDesign #HealthResearch

Small sample ≠ "don’t worry, I'll report the effect size.” With a small n, effect sizes are a rollercoaster: they can look large in one study and disappear in the next. That’s how false conclusions happen. A common mistake: “Not significant = no effect.” Not necessarily. It may simply mean the study had low power (too little data). So yes, effect size matters (a lot). But not alone. The reader have to understand all the context and uncertainty. Best practice: ✅ Report effect size + confidence interval (CI) + p-value ✅ And in some fields, also consider practical/clinical significance Small samples can be useful, but they require careful interpretation and the uncertainty should be in focus. In the plot: each dot is one simulated study at a given sample size; red dots are statistically significant (p < 0.05), and the dashed line shows the true underlying effect size. The study (madeup for demonstration) compared statisticians vs. mathematicians using a t-test, measuring how many times they used the word ‘approximately’ in the last month.

Small training datasets can make AI models in healthcare unreliable, misleading, and even harmful — yet most studies don’t justify how much data they use. 1️⃣ Most healthcare AI models skip explaining why their dataset size is sufficient — even though it’s critical for reliability. 2️⃣ Small sample sizes miss important patient groups, leading to biased and unfair predictions. 3️⃣ Predictions from small datasets are unstable: the same model trained repeatedly gives wildly different results. 4️⃣ With fewer data points, models struggle to separate real patterns from noise, lowering performance. 5️⃣ Calibration — how well predicted risks match actual outcomes — becomes highly inaccurate with small datasets. 6️⃣ This weakens clinical usefulness: even if a model says “high risk,” you can’t trust it’s accurate enough to guide treatment. 7️⃣ Evaluation suffers too: testing on small datasets gives overly optimistic or meaningless performance scores. 8️⃣ Even “large” datasets can have tiny effective sample sizes if key subgroups or outcomes are rare. 9️⃣ Simple tools like R’s pmsampsize and pmvalsampsize help calculate how much data is needed for training and testing. 🔟 Without enough data, AI can do more harm than good — especially if it gets deployed with false confidence. ✍🏻 Richard Riley (R²), Joie Ensor, Kym Snell, Lucinda Archer, Rebecca Whittle, Paula Dhiman, Joseph Alderman, Xiaoxuan Liu, Laura Kirton, Jay Manson-Whitton, Maarten van Smeden, Karel Moons, Krishnarajah Nirantharakumar, Jean-Baptiste Cazier, Alastair Denniston, Ben Van Calster, Gary Collins. Importance of sample size on the quality and utility of AI-based prediction models for healthcare. The Lancet Group Digit Health. 2025. DOI: 10.1016/j.landig.2025.01.013

Statistical Power and Sample Size — Why Your Study Might Miss a Real Effect Ever had a study show no statistical significance… but you knew the effect was real?  The culprit might not be the data — it could be low statistical power. When interpreting the results of a study, it’s easy to focus solely on whether the p-value crosses the 0.05 threshold. However, a non-significant p-value doesn’t always mean there’s no effect — sometimes, it means the study didn’t have enough statistical power to detect it. Understanding power and sample size is crucial for designing studies that can reliably uncover true effects. 🔍 What is Statistical Power? Statistical power is the probability that a test will detect an effect when there is an effect to detect. Power = 1−β Where: β (beta) is the probability of a Type II error — failing to reject the null when it's false. You can think of power as the sensitivity of your test. 📏 What Affects Power? - Sample size (larger = more power) - Effect size (larger = easier to detect) - Significance level (α) (e.g., 0.05) - Data variability (more noise = less power) 📊 Real-World Example: Testing a New Training Program Imagine you’re evaluating whether a new training improves employee performance: - You conduct the study with a small number of participants. - The p-value comes out as 0.09 — technically not “significant.” - However, the true effect might still be there; your study just lacked the power to detect it. This example illustrates why powering a study properly before collecting data is key to meaningful results. 🚨 Why It Matters Low statistical power increases the risk of missing real effects, leading to false negatives and potentially abandoning valuable interventions. It also contributes to irreproducible research and wastes time and resources. Properly calculating and achieving adequate power helps ensure your findings are robust and trustworthy. 📚 Further Reading: - Article: “Understanding Statistical Power: The Key to Better Research” - Research Paper: “The Role of Sample Size and Power in Experimental Design” 💡 Takeaway Statistical power is a vital concept that influences how you design studies and interpret “non-significant” results.  Always ask: Is my study powerful enough to detect what I’m looking for? Balancing sample size, effect size, and variability is the recipe for meaningful, reliable research conclusions. #Statistics #StatisticalPower #SampleSize #ResearchDesign #DataScience  #ExperimentalDesign #TypeIIError #DataAnalysis #ResearchMethods ♻️ Find this helpful? [Repost]  ✍️ Anything to add about this subject? [Comment]  👍 Nice post, Yashica! [Like]

🛑 Don't run A/B tests 📊 Unless you know exactly what you are doing. Let me explain... A charity recently came to me saying they had tested a new fundraising strategy in an A/B test. They (thought) they did everything right. They had two groups (a "control" and a "test"), they had run the campaign fairly and had analysed all the data with the right statistics. The result? No difference. The conclusion? Let's try something else. The problem? Their sample size was too small, so it was a complete waste of time. Why is the sample size important? The sample size of your experiment (number of cases or donors in each group) dictates how likely you are to detect an effect. The larger the sample, the better. The problem is often experiments are run with samples that are too small, meaning there is virtually 0 chance of detecting an effect. This is what happened with the charity above. So how much data do you need? Well it depends, but here are some rules of thumb. Say, you want to try a new campaign strategy to improve response rates. Assuming your current response rate is 10%, and that you are hopeful your test condition will improve this by 10% (to 11%). You would need a sample size of 7248, which means a total campaign size of nearly 14,500. Or, let's say you want to try a new ask strategy to increase gift amounts. Assuming your average gift size is $100 and that you are hopeful your test condition will improve this by 10% (to $110). You would need a sample size of 8306, which means a total campaign size of nearly 16,700. Hope that helps. Here is a handy calculator you can use to get more precise numbers for your own experiments 💻 https://lnkd.in/d58Ne84N

Determining an appropriate sample size is often an underestimated yet critical step in ensuring the validity and reliability of research findings. This document, Sample Size Determination, offers an in-depth exploration of the methodologies and considerations involved in calculating sample size for a variety of research designs. It addresses the balance between ensuring adequate statistical power and maintaining practical feasibility, a dilemma frequently encountered by researchers and humanitarian professionals alike. The guide delves into hypothesis-driven approaches and confidence interval methods, providing detailed formulas and practical examples tailored to specific research objectives. Whether estimating means, proportions, or differences between groups, this resource equips readers with the tools to justify sample size decisions, meet ethical requirements, and align with study goals. It also highlights common pitfalls in sample size calculation, offering strategies to avoid errors and improve the robustness of research designs. For humanitarian professionals managing evaluations or research in resource-constrained environments, this document is invaluable. By mastering the principles of sample size determination outlined here, practitioners can enhance the credibility of their studies, optimize resource allocation, and ensure that their findings contribute meaningfully to evidence-based decision-making.

📏 Honing Your Statistical Intuition: Sample Size Rules of Thumb When Calculations Aren’t Feasible In practice, we often work with fixed sample sizes. Someone hands you a dataset, or you inherit it from a lab, clinic, or program without any opportunity for prior sample size calculation. But just because you didn’t calculate the sample size doesn't mean you shouldn’t question whether it’s enough. Here are a few intuitive rules of thumb to guide you, grouped by type of analysis: 🔹 1. For Univariate Analysis (Estimates, Frequencies) For prevalence, means, or percentages: ✅ Follow the 30s Rule: Aim for at least 30 observations total (or per category for categorical data) for reliable estimates. Alternatively, ensure the relative standard error (RSE = [standard error / proportion] * 100) is below 30% (up to 50% in exploratory studies). Sparse data risks instability. 🔹 2. For Bivariate Analysis (Cross-tabulations, Chi-square) Asking, “Is there a difference between groups?” ✅ Aim for at least 5 observations per cell to avoid computational issues like singularity. E.g., 200 categories with 200 samples is a red flag 🚩. ✅ Use the ‘10 per degree of freedom’ rule: E.g., gender (2 levels) vs. socioeconomic class (3 levels) gives df = (2-1)*(3-1) = 2, so 20 observations. This rule, though informal, has backing from simulation studies. It helps ensure you’re not making unreliable inferences from sparse data, reflecting a Popper-esque skepticism: don’t draw conclusions from what can’t properly challenge your hypothesis. 🔹 3. For Exploratory Multivariable Regression Critical for reliability: ✅ Apply the N/10 Rule: Limit predictors to one per 10 events (not total sample, but cases/outcomes). E.g., 50 events = ≤5 predictors. Risks include overfitting, unstable estimates, and singular matrices. 🎯 Bottom line: Without formal calculations, judgment and intuition guide us. These rules aren’t perfect but offer a checklist to avoid pitfalls. #DataScience #Statistics #SampleSize #StudyDesign #StatisticalThinking #Epidemiology #RegressionAnalysis #ScientificRigor #QuantitativeResearch

*** The World of Sample Size *** **Understanding the Importance of Sample Size** **Why Sample Size Matters** - **Statistical Significance:** A small sample size can significantly skew research results. It heightens the risk of overlooking critical differences or effects within the data. Conversely, a larger sample size enhances the likelihood of achieving statistical significance, ensuring that your findings are not merely the result of chance. This means your results are more likely to be reliable and representative of actual patterns within the population. - **Confidence Intervals:** The sample size directly impacts the confidence intervals derived from the data. With larger sample sizes, confidence intervals narrow, resulting in more precise estimates. This precision is essential for conveying the uncertainty associated with the findings and assessing the reliability of the conclusions. - **Avoiding Errors:** - **Type I Error (False Positive):** This error occurs when a study incorrectly identifies an effect that does not exist, potentially leading to misleading conclusions. - **Type II Error (False Negative):** On the other hand, this error happens when a study fails to detect an effect that is present. Carefully planning the sample size can significantly decrease the chances of both mistakes, ensuring the research findings are accurate. **Key Factors in Choosing a Sample Size** - **Population Size:** The size of the population under study plays a critical role in determining the required sample size. For instance, a smaller sample may suffice in a small group, such as a class of students. However, a more extensive sample will be necessary for a larger population, like an entire country, to ensure reliable results. - **Effect Size:** If you expect subtle effects, such as modest differences in test scores, a larger sample size becomes essential to detect these nuances reliably. The smaller the anticipated impact, the more data you will need to reveal it confidently. - **Desired Confidence Level:** The confidence level you aspire to achieve also influences the sample size. Aiming for higher confidence levels, such as 95% or 99%, necessitates larger samples to ensure trustworthy results. - **Margin of Error:** When researchers want more precise data, they often seek smaller margins of error. However, achieving these levels of precision requires correspondingly larger samples. - **Variability:** The diversity within your study population also affects sample size requirements. A population exhibiting a wide range of characteristics or behaviors will require a larger sample to encompass this variability accurately. In essence, careful planning and consideration of sample size can dramatically influence the quality and applicability of research results across diverse fields. --- B. Noted

✨ 𝗦𝘁𝗮𝘁𝘀 𝗠𝗮𝗱𝗲 𝗘𝗮𝘀𝘆 (𝗳𝗼𝗿 𝗕𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝗶𝗮𝗻𝘀) #𝟱 "𝗦𝗮𝗺𝗽𝗹𝗲 𝘀𝗶𝘇𝗲: 𝘄𝗵𝘆 '𝗻' 𝗰𝗮𝗻 𝗰𝗵𝗮𝗻𝗴𝗲 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴." We often get excited about the stats method - but forget that even the best model can fail if your "n" isn't solid. ⸻ ✅ 𝗦𝗺𝗮𝗹𝗹 𝗻 𝗺𝗲𝗮𝗻𝘀 𝗺𝗼𝗿𝗲 𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲. The fewer the samples, the more room for randomness. Your estimate might swing wildly - and your p-values won't be as trustworthy. Think: Wide confidence intervals, high uncertainty, unstable fold changes. ⸻ ✅ 𝗟𝗮𝗿𝗴𝗲𝗿 𝗻 𝗵𝗲𝗹𝗽𝘀 𝗱𝗲𝘁𝗲𝗰𝘁 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝗯𝘂𝘁 𝗿𝗲𝗮𝗹 𝗲𝗳𝗳𝗲𝗰𝘁𝘀. Statistical power increases with more data. And you can start trusting those subtle signals that matter in biology. ⸻ ✅ 𝗪𝗵𝗮𝘁 '𝗻' 𝗿𝗲𝗮𝗹𝗹𝘆 𝗺𝗲𝗮𝗻𝘀 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻. In bulk RNA-seq, 'n' = samples. In scRNA-seq, 'n' ≠ cells - it's often the number of biological replicates. Using thousands of cells from 3 patients doesn't give you an n = 3000. ⸻ ✅ 𝗕𝗶𝗼𝗶𝗻𝗳𝗼 𝘀𝗽𝗶𝗻: 𝘄𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀  • Pseudobulk DE? Make sure your sample counts are enough per group.  • Clinical studies? One or two outliers can skew small cohorts.  • Rare diseases? You may need clever modeling (Bayesian, bootstraps) when n is limited. ⸻ ⚡ The takeaway: A good analysis starts before you open R. Ask: "Is my sample size enough for the story I want to tell?" Because sometimes the most powerful thing isn't your code - it's your 𝘀𝘁𝘂𝗱𝘆 𝗱𝗲𝘀𝗶𝗴𝗻.