HPLAB Learning Goals

Honours Physics Lab wiki --> here

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We (the instructors) present you (the students) with a list of "Learning Goals" in order to explain clearly what we intend for you to learn, and what our criteria will be for evaluations of your success, since part of our responsibility is to make such assessments, the results of which will have some impact on your subsequent careers. The actual outcomes of your learning experiences are sure to be different for every person and have nuances that we might never realize. We would like to know what you do experience, as well as what you would like to experience.

Hence this page, which will always be a work in progress. We will start things off with the official, published list of Learning Goals, but you may think of better descriptions based on your experience of the course. Edit. Add. Delete. We need your feedback, and this seems like an ideal way to mold a description that fits both our intentions and your experiences.

-- Jess 02:38, 7 September 2009 (UTC)

PREAMBLE:

The first year laboratory component of PHYS 107/109/SCIE 1 takes advantage of unique features of doing physics in a laboratory setting. Most importantly you will discover that science is not simply a static body of concepts and mathematics, but is based on empirical observation and experimentation. The laboratory is not primarily motivated by the aim to teach particular physics concepts or to reinforce what is taught in lectures and tutorials. Instead, the goal is to leave you with skills and attitudes that will be of value no matter what your later academic path may be. You will learn how to make observations and measurements, how to build models that fit those measurements, and derive meaning from the success or failure of those models. You will also learn a variety of technical skills including the use of particular equipment, carrying out well-established techniques, keeping thorough laboratory notes, a wide range of computer skills, statistical methods and the ability to communicate results and ideas. This environment beyond the textbook also brings in two key features of the real world: measurement uncertainty and the complex mix of phenomena that can often be taking place simultaneously in an experiment.

CORE LEARNING GOALS:

Making Measurements

You will learn how to take measurements, something that embraces a range of skills and attitudes in addition to learning specific equipment and procedures. The emphasis here is on the most broadly applicable skills.

• You will be able to analyze an experimental situation in order to identify the variables that might control the phenomenon being studied.
• You will be able to control variables as part of designing a course of action in an experiment.
• You will know what experimental uncertainty is.
• You will assign appropriate units to measured quantities.
• You will learn how to determine the precision of a measurement and to attach an experimental uncertainty to any measured value.

  This includes

  • Estimating uncertainty by actually studying distributions in the data
  • Uncertainty for the special case of a Poisson distribution
  • Uncertainty due to noise (e.g. in electronics)
  • Uncertainty due to instrumental precision
• You will be able to identify and analyze the sources of uncertainty in a measurement.
• You will be able to design measurements that minimize the sources of uncertainty.
• You will learn tactics for better data taking.

  These include

  • Covering a wide variable range quickly when possible
  • Evaluating data early and adjusting choices 'on-the-fly'
  • Evaluating whether or not magnitudes, units and precision are reasonable

Modelling Data

You will learn an essential step in the connection between empirical measurements and theory, which is modelling data. This again includes a number of technical skills as well as the problem of making the connection between discrete data and mathematical functions.

• You will learn what exponential growth, exponential decay, and power law scaling are.
• You will learn to make a two dimensional scatter plot of data on linear scales.
• You will be able to provide a description in words of such a graph.
• You will learn methods for linearizing data sets, including semi-log plots, log-log plots, and power law scaling.
• You will be able to extract parameters, their units and their meaning in situations where the plotting techniques above lead to a straight line.

Statistics and Data

A proper treatment of models and data involves some statistics.

• You will be able to evaluate the relative importance of numbers that have differing uncertainty.
• You will recognize whether numbers with some uncertainty are in agreement with one another.
• You will be able to perform a weighted least squares fit to a straight line and extract values and uncertainties of the parameters as well as their meaning.
• You will be able to perform a weighted non-linear least squares fit to a model and extract the parameters and uncertainty.
• You will be able to judge whether or not a model fits a data set.

Higher level skills and attitudes

There are meta-skills that might be considered the next level in your ability to handle an experiment.

• When confronted with a situation where more than one model plausibly fits the data, you will be able to devise a means of improving the experiment by improving precision, covering a wider range of variables, or making a prediction and designing a different experiment to test it.
• When confronted with a disagreement with an expected model, you will be able to offer a modification that is plausible and that can be tested further.
• In the same situation of disagreement, you will also be able to devise experiments to search for, and correct, hidden systematic errors.
• Having drawn a conclusion from an experiment, you will be able to devise a new experiment that further tests a successful model.
• You will be able to write a concise account summarizing an experiment.

TO BE ADDED OR EMPHASIZED MORE:

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TO BE DELETED OR DEEMPHASIZED:

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