Gain a comprehensive understanding of hypothesis tests and their application in solving real-world problems with this four-hour course on the foundations of inference in Python. Learn how to transition from descriptive statistics to confident decision-making by analyzing data and making sound conclusions. Explore the importance of proper sampling and how improper sampling can affect statistical inference. In this course, you will delve into a broad range of scenarios, including hypothesis tests for normality and correlation, as well as parametric and non-parametric tests. Utilize the SciPy library to run these tests and interpret their output for effective decision-making. Additionally, you will learn how to measure the strength of an outcome using effect size and statistical power, while avoiding spurious correlations through the application of corrections. Furthermore, this course will equip you with the skills to work with big data by employing simulation, randomization, and meta-analysis techniques. You will gain the ability to re-analyze results from other researchers and draw solid conclusions from diverse datasets. Upon completion of this course, you will possess the expertise to utilize big data in making principled decisions that leaders can rely on. Move beyond basic graphs and summary statistics to produce reliable, repeatable, and explainable results.