Kenneth Schafer’s Post

We are not guided by expectations or beliefs in conducting our science. What we do focus on is data. In other words, reliable and methodologically acquired information based on facts. #HardData #ScientificMethod #ScientificConclusion #ScienceInformation

Reviewing assumptions is crucial for causal inference. Why? To make sure they're met! It's also important to determine if the assumption seems reasonable. An article I recently came across discusses this for the ICH E9 (R1) addendum on estimands. Vansteelandt & Van Lacker discuss parts of the addendum through a causal inference lens using the counterfactual approach. I work mostly with observational data. However, it can be helpful to think of things in a RCT framework then how that could apply to observational data. (Credit to Matt Tenan, for commenting this on a previous post. I find that approach quite useful) While reading this article, I tried to think of ways it could apply to observational data. Let me know in the comments your thoughts! #CausallyCurious #RealWorldEvidence #CausalInference #RealWorldData [Link: https://lnkd.in/euc23s84]

Poster is up! I’m glad to speak with researchers at the Royal Statistical Society on Sequential Hypothesis Testing. I discuss the importance of the distribution precision as the goal of a stop criterion (Kruschke 2014). I also present a new variant of precision based stop criterion that I’m working on that fills a gap in the literature with the working title: “Enhanced Precision is the Goal” The poster is available at https://lnkd.in/e2xMddwY #sequentialhypothesistesting #hypothesistesting #datascience #statistics

This document discusses the use of propensity score matching (PSM) and related techniques for improving causal inference in observational studies. It highlights challenges such as model misspecification and selection bias and proposes methods to achieve better covariate balance between treatment and control groups. The concepts of Average Treatment Effect (ATE) and Average Treatment Effect on the Treated (ATT) are explored, emphasizing the need for assumptions like strong ignorability and common support. Balancing techniques and their impact on reducing model dependence are discussed, with visualizations comparing raw and balanced data for linear and quadratic models. #statistics #causalinference

Statistical inference Statistical inference is the process of using statistical methods to make conclusions or decisions about a population, based on data from a sample. Three Types of Statistical Inference: 1. Estimation: Estimating population parameters (e.g., mean, proportion) from sample data. Example: Estimating the average height of a population based on a sample of 100 people. 1. *Hypothesis Testing*: Testing hypotheses about population parameters based on sample data. Example: Testing whether a new medicine is effective in curing a disease. 1. Confidence Intervals: Constructing intervals to estimate population parameters with a certain level of confidence. Example: Constructing a 95% confidence interval for the average IQ score of a population. #dataanalysis #timeseriesforecating #statistical_inference #spss

𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐓𝐡𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭 𝐎𝐟 𝐁𝐚𝐲𝐞𝐬𝐢𝐚𝐧 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠. Bayesian A/B testing is an approach to experimental design and analysis that incorporates Bayesian statistical methods to compare different variations or treatments in an A/B test. Unlike traditional frequentist A/B testing, which relies on hypothesis testing and p-values, Bayesian A/B testing uses Bayesian inference to quantify uncertainty and update beliefs about the treatment effects based on observed data.

Hello LinkedIn! 🌟 Welcome to Day 31 of our statistics learning journey! Today, we’re exploring the Mann-Whitney U Test. • Mann-Whitney U Test: A non-parametric test used to compare differences between two independent groups when the dependent variable is either ordinal or continuous but not normally distributed. • When to Use: Comparing Two Groups: Ideal for comparing the central tendency (median) of two independent groups, especially when data is not normally distributed. Alternative to t-Test: Use when the assumptions for an independent t-test are not met. Example: Imagine comparing recovery times for patients receiving two different treatments. If recovery times are not normally distributed, the Mann-Whitney U Test can help determine if there’s a significant difference between the treatment groups. Happy learning! Stay connected. 📊✨ #Statistics #DataScience #MannWhitneyUTest #NonParametricTest #HypothesisTesting #LearningJourney #StayCurious

The Range and Scientific Value of Randomized Trials admin To learn how to implement #economic_evaluation_techniques (#CEA and #CUA) using decision analytic models, including #decision_trees and #Markov_models in a one-to-one setting: https://lnkd.in/dUcpjq6p.

Using Quirkos, MaxQDA, NVivo or Atlas.ti? Why not try Dedoose! Compare your experience during a free webinar overview with Kurt today at 1pm EST: https://lnkd.in/ewRHHGHa Download a free 30-day trial to follow along (or analyze your own data!): dedoose.info/signup #qualitative #quantitative #mixedmethods #research #data #qdas #analysis #marekting #academics

🎥 Replay the webinar Webinar : How to do omics statistical analysis in Constellab ? In this event, Thibault Etienne showed how to perform omic/multi-omic data analysis through machine learning approaches. By the end of this webinar, you will know how to analyze your biological data in Constellab using advanced learning tools. #omic #constellab #data #research #webinar #ia https://lnkd.in/gaY3eijS