Understanding Statistical Power in Research Design

Part 1: Analytica tip: what influence diagram are and are not Dependencies between variables can be deterministic or probabilistic, often representing relationships such as earnings being a function of revenues and costs, or empirical relationships like calculating standard deviation from data. While these dependencies effectively describe the flow of information during computation, they do not necessarily depict the flow of materials, money, or causal relationships. Check back tomorrow for part 2, as this video is part 1 of a 4 part series.

Do you want to describe a single variable ?- use a BAR CHART A SINGLE BAR CHART This is basically used to visualize a single categorical variable (univariate analysis) On the horizontal axis, we have the variable. On the vertical axis, we have the frequency.

Hypothetically speaking, let's say you're conducting an experiment. You have five dependent variables that you intend to measure and compare between groups. Your theory says that one group should have higher mean scores than the other group on all five dependent variables. You conduct 5 two-sample t tests on your data. Which of the following two outcomes represents better support for your theory? 1) All 5 tests result in p = 0.10 2) 4 tests result in p = 0.5, 1 test results in p = 0.005

✴️ Pleased to introduce our new paper: Dynamic Reward Weighting 🔗: https://lnkd.in/eXyPJUSu - Rebalance multiobjectives during training through dynamic reward weighting - Build Pareto-dominant front over static baselines across online RL algorithms, datasets, and model families - Faster convergence rate

Model Performance Metrics — How to Choose the Right One A practical guide to evaluation metrics for classification, regression, ranking, and business use cases. https://lnkd.in/d5m7adFg

How do you build a Theory of Change that actually guides decisions? This practical Fokus guide is a must-read. It shows you how to move beyond activity lists and explain how change happens in your context. That is... 📍 Map non-linear pathways with clear preconditions (not just outputs). 📍 Make assumptions explicit and testable, with signals and data sources. 📍 Pair ToC with the logframe: ToC = why/how; logframe = what/when. 📍 Set lean indicators and build a monitor-reflect-adapt loop. ------ Attend the Theory of Change webinar in September to learn more. As usual spots are limited so secure yours NOW! https://lnkd.in/eszvBaWh #TheoryOfChange

𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗗𝗮𝘁𝗮: 𝗪𝗵𝘆 𝗦𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼-𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗔𝗿𝗲 𝗡𝗼𝘁 𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹 Many researchers dive into time-series analysis with impressive models — but ignore the foundations. That’s where the damage begins. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝘄𝗲 𝘀𝗲𝗲 𝗳𝗮𝗿 𝘁𝗼𝗼 𝗼𝗳𝘁𝗲𝗻: 𝟭. Running regressions on non-stationary data — leading to spurious relationships 𝟮. Skipping unit root tests — assuming trend-stable data is good enough 𝟯. Confusing correlation with co-integration — without testing long-run equilibrium 𝟰. Ignoring order of integration (I(0), I(1)) — and misapplying models like ARDL or VAR 𝟱. No justification for differencing — losing long-term dynamics in the process Time-series modeling without testing for stationarity and co-integration is like building on quicksand — it might look stable, but it won’t hold under scrutiny. #TimeSeriesAnalysis #Econometrics #Stationarity #Cointegration #ARDL #RegressionModels #QuantitativeResearch #ResearchAndReport #PhDMethods #AcademicPublishing #StatisticalRigor

We used Claude to optimize its own tools by running evaluations and reviewing its own transcripts. This revealed three key principles: • Use clear, descriptive tool names. • Return only essential context rather than full database entries. • Build fewer, more focused tools rather than comprehensive sets. Read more about our process: https://lnkd.in/dWm2VVuY

The fact is that Deming's causal analysis was very weak, yet he provided extensive prescriptions. In contrast, my work features extremely strong causal analysis to guide people in developing their own prescriptions. The latter approach is a significant improvement because it engages people to think more deeply about their situation and how to improve. 👇 1️⃣ 𝑻𝒉𝒆 𝑻𝒓𝒊𝒖𝒎𝒑𝒉 𝒐𝒇 𝑪𝒍𝒂𝒔𝒔𝒊𝒄𝒂𝒍 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝑶𝒗𝒆𝒓 𝑳𝒆𝒂𝒏 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 https://lnkd.in/en6s-nSh 2️⃣ 𝑰𝒓𝒓𝒂𝒕𝒊𝒐𝒏𝒂𝒍 𝑰𝒏𝒔𝒕𝒊𝒕𝒖𝒕𝒊𝒐𝒏𝒔 https://lnkd.in/eGe69Ubt 3️⃣ 𝑴𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕 𝑴𝒚𝒔𝒕𝒆𝒓𝒊𝒖𝒎 https://lnkd.in/eiUKRDym 4️⃣ 𝑻𝒉𝒆 𝑨𝒆𝒔𝒕𝒉𝒆𝒕𝒊𝒄 𝑪𝒐𝒎𝒑𝒂𝒔𝒔 https://lnkd.in/e6DfRvXq 5️⃣ 𝑨 𝑪𝒉𝒂𝒏𝒈𝒆𝒅 𝑷𝒆𝒓𝒔𝒑𝒆𝒄𝒕𝒊𝒗𝒆 https://lnkd.in/eJY3BUJ4 6️⃣ 𝑻𝒉𝒆 𝑾𝒐𝒓𝒌𝒎𝒂𝒏𝒔𝒉𝒊𝒑 𝒐𝒇 𝑳𝒆𝒂𝒅𝒆𝒓𝒔 https://lnkd.in/etZyyAsn