Confidence Intervals and Statistical Estimation with R

When analyzing data, point estimates like the sample mean only tell part of the story. To make reliable, data-driven decisions, you must understand how to quantify uncertainty and estimate true population parameters. This text-based course guides you through the foundational principles of statistical estimation, teaching you how to calculate, interpret, and communicate confidence intervals using R. You will start with core statistical concepts, learning how to define populations, samples, and the central limit theorem. From there, you will transition into hands-on R programming, applying modern tidyverse workflows to clean data, run statistical tests, and construct intervals for both means and proportions. You will also learn to avoid common misinterpretations of confidence intervals and apply modern simulation-based bootstrapping techniques to estimate uncertainty when traditional assumptions fail. What you'll learn: - Understand the foundational theory of statistical estimation, point estimates, and margin of error - Calculate confidence intervals for means and proportions using standard formulas in R - Interpret the true meaning of confidence levels and avoid common statistical misconceptions - Apply bootstrapping and simulation methods in R to estimate parameters for non-normal distributions - Use modern R package workflows to summarize data and extract clean statistical outputs - Evaluate how sample size and variability directly impact the width of your intervals This course is designed for beginners, data analysts, and researchers who want to build a solid foundation in statistical inference. No prior experience with advanced statistics is required, though a basic familiarity with R syntax is helpful. Acquire the skills to confidently measure uncertainty and back up your data insights with statistical rigor.