Welcome to Physical Sciences 2 lab! We're very excited about the labs for this course, and we hope you will be, too. Everything about the labs has been newly designed for a great educational experience with a minimum of annoying busywork. We've had a lot of fun working on the labs, and hopefully, you will have a lot of fun doing them. By this time you should all have sectioned for a lab time assignment. If you haven't, or if you don't remember your lab section time, please contact Kirill immediately: [email protected]. Lab 1 will run next week, from Tuesday, October 3 to Thursday, October 5. Before you show up to your first lab next week, we would like you to do three things: 1. Download the Logger Pro Software. Logger Pro is the data collection and analysis software we will be using for all of the labs in this course. It's very easy to use and powerful, and is available both for Windows or Macintosh platforms. The site license agreement allows any Harvard student to freely download and use the software. (If you don't have a PC or a Mac, or don't want to put Logger Pro on your own computer, you can use one of the computers in the Science Center computer labs.) The program can be downloaded from the HASCS Software Download Page: http://www.fas.harvard.edu/computing/download. Either version 3.4.5 or 3.4.6 is okay; 3.4.6 is the latest; as of this writing, 3.4.5 is the version available from HASCS, but we're told they are working on getting 3.4.6 up there. 2. Learn to use Logger Pro. We recommend you go through some of the tutorials that come with the software. To do so, go to File-Open and then under the folder labeled Experiments, find the subfolder called Tutorials. Tutorial #1 is a quick overview; #5 has information on entering data; #7 is a very brief summary on working with graphs; and #9 teaches you how to analyze data using curve fitting. Some of the other tutorials are also useful, but they require one or more sensors connected so that you can learn how to take data. 3. Read the attached handout, "An Introduction to Measurement and Uncertainty." This document contains ideas which will be new to many of you, even those of you with a background in statistics, but we have essentially tried to boil down the most important things you need to know about doing quantitative experimental science and put them in one place, so it is very important; we will be using the ideas from this document over and over throughout the labs this semester. If you have any specific questions about the document, please post your questions to the Lab Discussion Page on the course website, or contact your Lab TF. That's it! We look forward to a semester of fun, excitement, and instruction in the labs. See you next week!1 Physical Sciences 2 and Physics 11a An Introduction to Measurement and Uncertainty 1. Measurement and Uncertainty In the laboratory portion of this course, you will perform experiments and make observations. You should distinguish between two types of observations: qualitative and quantitative observations. Although qualitative observations are an important aspect of experimental science (e.g., “I connected the battery and smoke started pouring out of the device”), we will focus on quantitative observations, or measurements. You will make measurements using various measuring devices, and report the values of these measurements. Physical theories, such as Newton’s laws of motion, make quantitative predictions about the outcomes of experiments: if we drop a ball from a height h above the ground, Newton’s laws predict the speed of the ball when it strikes the ground. In order to test, refine, and develop our physical theories, we must make quantitative measurements. Although you make measurements every day—after all, a clock is a device that measures time—you probably do not give much thought to the process of measurement. The following schematic should help you think about this process: The physical system(what we measure)is described by certainparameters, such asposition, time, velocity,mass, force, etc.The measuring device(takes a measurement)could be a stopwatch,ruler, balance,thermometer, etc.The experimenter may be "a part of the device."The measurement(what we record)must have three things:• numerical value• estimated uncertainty• units Within the paradigm of classical physics, we consider the parameters of the physical system to be defined to infinitely high precision. Any measuring device, however, has some limits on the precision of its measurements. For instance, you may measure time using a digital stopwatch that records time to the nearest millisecond. A measuring device observes a physical system and records a measurement. When you measure length using a ruler, the ruler alone is not a complete measuring device: you must interpret the markings on the ruler and record the measurement, so you are a part of the2 measuring device. A thermometer connected to a computer is a complete measuring device, since the computer records the measurements. All measurements involve some uncertainty, or error. Physicists use the term error not to describe mistakes (“I dropped the thermometer and it broke”) but to describe the inevitable uncertainty that accompanies any measurement. When we report a measurement, we must include three pieces of information: the numerical value of the measurement, the units of the measurement, and some estimate of the uncertainty of the measurement. For example, you might report that the length of a metal rod is 13.2 ± 0.1 cm. In the first lab activity of this course, we will try to explore exactly what is meant by uncertainty. We distinguish between two types of error in measurement: systematic error and random error. The following illustration shows examples of these two types of error: "true" valuemeasuredvalueslarge systematic errorsmall random errorsmall systematic errorlarge random error The set of measured values on the left exhibit a large systematic error: they are all lower than the true value of the parameter. The set of measured values on the right exhibit a small systematic error: they are, on average, neither higher nor lower than the true value of the parameter. However, the measured values on the right have more random error than those on the left: they vary more from one measurement to the next. You may have heard the terms precision and accuracy used to describe measurements. A