Measurement Errors

The Role of Measurement Error

On the first exam, your score reflects two things:

How we are handled the exam:

Your exam was graded by 3 different people although all have the same exam key. The exams were randomly sorted as they were handed in and the 3 graders randomly picked a pile. Each pile has 35 exams.

Since the intrinsic distribution of exam scores will be normal (bell- shaped) then by the sampling principles established earlier we know that 30 samples is enough to accurately reflect the distribution.

Therefore, each of the 3 piles should have the same mean and dispersion. Differences in grading style, however, will cause each grader to have a slightly different mean and dispsersion.

es will then manifest itself by different mean scors for different graders. This can be corrected for easily.

er manifests itself by different dispersions for different graders. This is due to random errors associated with grading the questions and assigning a point value in a lightly subjective manner.

For this exam, your dispersion score was calculated by the instructor after er and es had been determined. In principle, this needs to be done for every exam you take in large classes. Most instructors don't do this, period (its too much work).

You should always demand that your instructors prove to you that in the case of multiple graders on the exam, no bias exists

For this exam:

Introduction to Measurement Errors:

In class exercise.

On a piece of paper, write down your estimates for the following quantities and give them to one of the TAs.

Understanding the role of measurement errors is crucial to proper data interpretation. For instance, the measured dispersion in some distribution represents the convolution of

In general, you only care about the intrinsic dispersion in some distribution. That is, you don't want to have the dispersion dominated by measurement error or poor precision because then you can't draw any valid conclusion.

Example: Column 1 contains the data that was measured with good precision. That is, the measuring error of the instrument was less than 0.1. Column 2 represents the same data that was measured with and instrument that had a measuring error of +/- 1 unit:

  • The first column yields a dispersion of 0.23
  • The second column yields a dispersion of 1.44

    Clearly the first column is a better measure of the intrinsic distribution of the sample than the second column. Essentially the numbers in the second column are meaningless.

    In principle, every measurement has an error associated with it and hence a measurement is only as good as its error. Knowing the size of measuring or sampling errors is often difficult.

    For some kind of sampling, error estimation is straight forward. For instance, opinion poll sampling has an error that depends only on the Number of people in the sample. This error has to do with counting statistics and is expressed as

    Square Root of N divided by N

  • For a sample of 16 people, the error would be 4/16 = 25%. This a large error since the range of YES vs NO is from 0-100% if 12 people answered yes and 4 people answered no then your result would be:

  • For a sample fo 1000 people, the error would be SQRT(1000)/1000 = 33/1000 = 3%. If 750 answered yes and 250 answered no then your result would be:

    Conclusion: Always ask what the measuring errors are!!!


    Data reliability involves the determination of the extent to which the data sample can address the question. For data samples widely distributed over time, one can raise the issue of the reliability of data taken with older instruments.

    Data reliability is an issue when you can not measure a phenomena directly but instead you have to use some tracer.

    For example:

    What is the best way to measure the extent of urban air pollution?

    What is the best way to measure acid rain?

    Another good example of the issue of data reliability is evidence for Global Warming:

    Issues to raise:

    Let's see the data:

    Here is some Global Data whose reliability is high. There is no doubt that CO_2 in the atmosphere is increasing and is now at unprecedented levels

    Methane concentration from ice core data!

    This shows strong exponential growth in the last 100 years or so - consistent with growth in the world's population.

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