In this module, you will learn how to define null and alternative hypotheses, which form the foundation of any hypothesis test. You’ll explore the concepts of type I and type II errors and understand their impact on decision-making. The lesson will guide you in distinguishing between one-tailed and two-tailed tests, helping you choose the appropriate test for different scenarios. Finally, you will learn to interpret p-values and assess statistical significance, enabling you to draw meaningful conclusions from data and make informed decisions based on statistical evidence.

This module explores the fundamental concepts of sampling distributions and their crucial role in statistical inference. You'll investigate how samples drawn from the same population naturally vary, creating a distribution of statistical measures rather than a single fixed value. Through hands-on examples, you'll learn to distinguish between sample statistics (such as means and proportions) and their underlying distributions, gaining insight into how these sample values fluctuate around population parameters. We'll place special emphasis on the distribution of the sample mean, examining its properties and significance as a cornerstone of statistical inference. The module culminates with an exploration of the central limit theorem—one of statistics' most powerful principles—which allows us to make reliable approximations of sampling distributions regardless of the original population's shape. By understanding these concepts, you'll develop the essential foundation needed to construct confidence intervals, perform hypothesis tests, and make data-driven decisions in the face of uncertainty.

This module explores how we bridge the gap between sample data and population parameters through statistical estimation. We begin with point estimation, where single values from our sample serve as our "best guess" for unknown population parameters. We'll examine various point estimators and their properties before expanding to confidence intervals, which provide a measure of precision that point estimates lack. You'll learn how confidence levels represent the reliability of our estimation procedure and explore the critical relationship between sample size and interval width. The concepts of margin of error and precision will be central to our discussions, showing how larger samples typically yield narrower intervals and more precise estimates. We'll also address common misinterpretations of confidence intervals to ensure proper application. Throughout the module, we'll apply these techniques to real-world scenarios across disciplines, demonstrating how statistical intervals enable data-driven decisions with quantified uncertainty. Whether estimating population means or proportions, these methods provide a systematic approach to making inferences with incomplete information—a fundamental skill in statistical analysis.

In this module, you’ll learn how to estimate unknown population values using sample data through the construction of confidence intervals. These intervals provide a range of plausible values for population parameters and help quantify the uncertainty associated with your estimates. We’ll begin with methods for large samples, where the z-distribution can be used to construct confidence intervals for population means and proportions. Then, we’ll move on to small samples, where we use the t-distribution to account for greater uncertainty due to limited data. You’ll also explore the use of one-sided confidence intervals, which allow you to estimate just an upper or lower bound when needed—such as showing a minimum requirement is met or a maximum is not exceeded. By the end of the module, you’ll be able to select the appropriate confidence interval method based on your data, calculate interval bounds, and interpret the results in real-world situations.

This module explores three essential statistical methods for comparing population parameters: the Two-Sample Z-Test, the Two-Sample T-Test, and the Two-Proportion Z-Test. These tests are critical for evaluating whether differences between two groups—whether means or proportions are statistically significant. Together, these tools enable learners to analyze real-world scenarios, ranging from educational interventions to consumer preferences—by forming hypotheses, calculating test statistics and p-values, and making informed, data-driven decisions.

This module introduces One-Way ANOVA, a method used to compare three or more group means in a statistically valid way. You’ll learn how ANOVA partitions total variability into components, how to test for group differences using the F-statistic, and how to follow up with Tukey’s post-hoc procedure to identify which groups differ. The focus is on both statistical interpretation and practical application in engineering and experimental contexts.

In this final module, you’ll bring together everything you’ve learned in this course to analyze real-world case studies, reflect on your learning, and communicate your statistical insights effectively. You'll apply inferential methods like confidence intervals, hypothesis testing, ANOVA, and correlation analysis to authentic data sets from medicine, geology, and finance. The emphasis is now on synthesis—integrating methods, interpreting results with clarity, evaluating the assumptions behind statistical tests, and making informed decisions. You’ll demonstrate this in your group video presentations, offer peer feedback, and participate in a discussion on how your thinking and skills have evolved. This module also reinforces the importance of clear statistical communication—how to translate findings into understandable, actionable conclusions for different audiences.