Intro to Experimentation

Intro to Experimentation - Data Acquisition And Analysis...

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Data Acquisition And Analysis Objective: To gain familiarity with some of the measurement tools you will use in lab this semester. To learn how to measure distance with a motion sensor and force with a force sensor. To acquire experimental data, present it both statistically and graphically and become skilled at its analysis and interpretation. Apparatus: Motion sensor, force sensor, LabPro interface, computer & keyboard Introduction This lab serves as an introduction to the tools you will use to gather and analyze experimental data. Data analysis is important in any scientific, technical or health-care discipline, where it is used for critical decisions and thinking. In addition, the familiarity you develop with these instruments and software will come in handy when they are used many times throughout the semester. In the first part of the lab you will generate data by measuring your own reaction time; your lab partner will then repeat the experiment and you will analyze the data and compare your results. In the second part of the lab, you will tinker with a motion sensor and a force sensor to become familiar with them. Theory of Uncertainty and Measurement A scientist or engineer collects experimental data by taking measurements. You have certainly collected experimental data in your life – perhaps by finding out how much you weigh, how tall you are, or how fast you have run a certain distance. Let's assume that you know your height to be 5'6” (168 cm), your weight 140 lb (64 kg) and your time in the 100 meter dash 12.16 seconds – how valid are these numbers? Are you exactly 168 cm tall, and not 168.0625 cm (5'6 1 / 8 ”) tall? How does your 140 lb weight as measured on your $20 bathroom scale compare to your weight measured on a calibrated pharmaceutical scale used to weigh the ingredients at a manufacturing plant? To answer these questions, you will need to understand three basic concepts – uncertainty, precision and accuracy. You may have used the last two interchangeably in everyday life, but here in the laboratory we will make an important distinction between the two. Precision and accuracy are two different things - you can have data with high precision but low accuracy, and vice versa.
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Uncertainty: There are two types of uncertainties you will encounter in the lab: Systematic Uncertainties and Random Uncertainties . Systematic uncertainties cause a measurement to be skewed in a certain direction, i.e., consistently large or consistently small. For example, weighing yourself repeatedly on a bathroom scale that has has an initial reading of 20 lbs will result in your always appearing to be heavier than you actually are. This form of uncertainty can be removed, once identified – in this case you can just zero the scale using the little ridged knob (calibration).
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