Why precision matters in reference materials

🎯 Let's Talk Linguistic Precision in the Age of AI As generative AI becomes embedded in writing programs and literature search databases, I've noticed something concerning: the blurring of critical linguistic distinctions that signal evidence strength. Consider the consequences of an AI outputting "proves" for correlational findings, or "suggests" for experimental results. 🙀 Here's a practical guide to maintain precision in research writing: 🧪 𝐂𝐚𝐮𝐬𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 (𝐒𝐭𝐫𝐨𝐧𝐠 𝐄𝐯𝐢𝐝𝐞𝐧𝐜𝐞) 𝘥𝘦𝘮𝘰𝘯𝘴𝘵𝘳𝘢𝘵𝘦𝘴 𝘦𝘴𝘵𝘢𝘣𝘭𝘪𝘴𝘩𝘦𝘴 𝘳𝘦𝘴𝘶𝘭𝘵𝘴 𝘪𝘯 𝘥𝘪𝘳𝘦𝘤𝘵𝘭𝘺 𝘤𝘢𝘶𝘴𝘦𝘴 𝘭𝘦𝘢𝘥𝘴 𝘵𝘰 🔍 𝐐𝐮𝐚𝐬𝐢-𝐂𝐚𝐮𝐬𝐚𝐥 (𝐒𝐭𝐫𝐨𝐧𝐠 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐚𝐥) 𝘴𝘵𝘳𝘰𝘯𝘨𝘭𝘺 𝘴𝘶𝘨𝘨𝘦𝘴𝘵𝘴 𝘤𝘰𝘯𝘴𝘪𝘴𝘵𝘦𝘯𝘵𝘭𝘺 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘴 𝘪𝘴 𝘭𝘪𝘬𝘦𝘭𝘺 𝘵𝘰 𝘤𝘢𝘶𝘴𝘦 𝘵𝘺𝘱𝘪𝘤𝘢𝘭𝘭𝘺 𝘱𝘳𝘦𝘤𝘦𝘥𝘦𝘴 𝘳𝘦𝘨𝘶𝘭𝘢𝘳𝘭𝘺 𝘢𝘤𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 📊 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐢𝐯𝐞 (𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧) 𝘪𝘴 𝘢𝘴𝘴𝘰𝘤𝘪𝘢𝘵𝘦𝘥 𝘸𝘪𝘵𝘩 𝘤𝘰𝘳𝘳𝘦𝘭𝘢𝘵𝘦𝘴 𝘸𝘪𝘵𝘩 𝘤𝘰𝘳𝘳𝘦𝘴𝘱𝘰𝘯𝘥𝘴 𝘵𝘰 𝘤𝘰-𝘰𝘤𝘤𝘶𝘳𝘴 𝘸𝘪𝘵𝘩 𝘵𝘦𝘯𝘥𝘴 𝘵𝘰 𝘷𝘢𝘳𝘺 𝘸𝘪𝘵𝘩 ⚖️ 𝐓𝐞𝐧𝐭𝐚𝐭𝐢𝐯𝐞 (𝐋𝐢𝐦𝐢𝐭𝐞𝐝 𝐄𝐯𝐢𝐝𝐞𝐧𝐜𝐞) 𝘮𝘢𝘺 𝘴𝘶𝘨𝘨𝘦𝘴𝘵 𝘢𝘱𝘱𝘦𝘢𝘳𝘴 𝘵𝘰 𝘱𝘳𝘦𝘭𝘪𝘮𝘪𝘯𝘢𝘳𝘺 𝘦𝘷𝘪𝘥𝘦𝘯𝘤𝘦 𝘪𝘯𝘥𝘪𝘤𝘢𝘵𝘦𝘴 𝘴𝘦𝘦𝘮𝘴 𝘵𝘰 𝘩𝘪𝘯𝘵𝘴 𝘢𝘵 💭 𝐒𝐩𝐞𝐜𝐮𝐥𝐚𝐭𝐢𝐯𝐞 (𝐓𝐡𝐞𝐨𝐫𝐞𝐭𝐢𝐜𝐚𝐥) 𝘮𝘪𝘨𝘩𝘵 𝘵𝘩𝘦𝘰𝘳𝘦𝘵𝘪𝘤𝘢𝘭𝘭𝘺 𝘤𝘰𝘶𝘭𝘥 𝘱𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭𝘭𝘺 𝘩𝘺𝘱𝘰𝘵𝘩𝘦𝘵𝘪𝘤𝘢𝘭𝘭𝘺 𝘮𝘢𝘺 𝘤𝘰𝘯𝘤𝘦𝘪𝘷𝘢𝘣𝘭𝘺 𝘤𝘰𝘶𝘭𝘥 𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘺 𝘮𝘪𝘨𝘩𝘵 Think: Theoretical frameworks, hypotheses 🤔 Why This Matters: When AI tools use "proves" instead of "correlates with," they blur the lines between correlation and causation. When they say "demonstrates" for preliminary findings, they oversell uncertainty. These distinctions aren't just academic - they're fundamental to scientific integrity. #ResearchMethods #AcademicWriting #AI #DataScience #ResearchCommunity #Science Note: Visual made with napkin.ai.

🚨New blog post📝: Your Study Is Too Small (If You Care About Practically Significant Effects) Seeing more discussions about MCIDs and effect size confidence intervals on here lately, so I wrote up a quick explainer on why this matters more than most people realise. I focus mostly on why your goal - effect estimation - can be in conflict with your approach - power analysis for hypothesis testing. Most studies are designed to detect effects, but not to precisely estimate them. This creates a blind spot that affects how we interpret research across medicine, psychology, business, and policy. Here's a concrete example: You run a study testing if a new training program improves performance. You get Cohen's d = 0.40 with p < 0.05. Success, right? Not so fast. Your confidence interval might be [0.12, 0.68]. What this actually tells us: ✅ "There's probably an effect" ❌ "But it could be anywhere from trivially small to quite large" If your research question is "Should we implement this training program population-wide?" you need to know whether the effect is meaningfully large, not just whether it exists. The solution is precision-focused sample sizes: Instead of asking "How many participants do I need to detect an effect?" ask "How many do I need to estimate it precisely?" For the same study: Standard approach: n = 100 per group → CI spans 0.56 units (total N = 200) Precision approach: n = 196 per group → CI spans 0.40 units (total N = 392!!!) Bigger samples aren't just about finding effects - they're about understanding them well enough to make informed decisions. Whether you're evaluating medical treatments, educational interventions, or business strategies, precision matters as much as statistical significance. See full breakdown at the link below. (Also included is a lovely illustration of the issue by Adrian Olszewski; go give him a follow also!) #Research #DataScience #Statistics #MCID #EffectSize #Evidence #DecisionMaking #PowerAnalysis #Equivalence #NonInferiority #EstimationStatistics #Precision #Psychology #SampleSize #NHST https://lnkd.in/eRAWHSCV