Introduction to Laboratory Physics

The goal of this course is to give you practical knowledge of basic statistical and data analysis tools that you will need to do research in physics and/or astronomy. In particular, we will be focusing on why we use the data analysis methods and techniques that you will be learning. All of the instructors of the course had the experience that we never really understood many of the data analysis and statistical methods we learned as undergraduates until we had to use them in our own research. Top-level goals - you will learn how to: 1) use python and jupyter notebooks to do simple data analysis and make plots 2) assign an error (uncertainty) to an experimental measurement 3) have a defensible result from an experiment and 4) identify and quantify statistical and systematic errors in an experimental measurement. You will learn these techniques to learn that will help reach the goal: 1) finding mean, variance, and standard deviation of discrete and continuous data sets 2) error propagation, 3) least squares curve fitting and 4) how to use distributions to predict statistical spreads in data (in this class, primarily Gaussian and Poisson distributions). We will use real-world modern data sets as examples, such as measurements of the expansion rate of the universe, searches for dark matter particles in the LZ experiment, estimating the distance to distant galaxies using data from the Sloan Digital Sky Survey, and the analysis of electron microscopy images to estimate the Fermi energy of Copper.We believe an active learning approach, where we spend more time understanding and discussing the problems that these methods are intended to solve will help you to a deeper understanding of both the tools, and why we use them for data analysis. Required for Physics and Engineering Physics majors. Corequisite: Physics 81.This course was offered as PHYSICS 67 prior to Academic Year 2022-2023.