In any experiment, two types of error will inevitably be
encountered: systematic errors and random errors. The ability to identify,
eliminate, and subtract these types of error is crucial to obtaining reliable
data. This page is a description of the terms "systematic error" and "random
error." It will also describe some of the methods the MAP scientists are using
to minimize experimental and systematic errors.

Random Error
- There is some amount of random error in every measurement we take. This is
partially due to the sensitivity of our instruments and partially due to human
error. For example, if you were to measure the width of your computer screen
right now, you might come up with a width of 26.7 centimeters. Now, how can
you be sure that 26.7 cm is the exact width of your screen? You
cannot. Several sources of error make an exact measurement impossible in this
case: you might not have looked at each end of your ruler straight-on, causing
errors due to parallax. The ruler might not have been exactly parallel with the
top and bottom of the screen, resulting in a slight overmeasurement of the
screen. If the width fell between the notches on the ruler, as is the case
with 26.7 cm, the extra 0.7 cm measurement is based on human judgment and could
possibly be off by one or two millimeters. This list of error sources goes on
and on, and this is a fairly simple measurement. The objective in any
scientific experiment such as MAP is not only to collect and analyze data, but
to collect data will the smallest possible error.
Systematic Error
- In the above example, the errors are random. Those errors such as error
due to human judgment are random errors. However, other errors concern the
instruments themselves: the ruler itself probably is not calibrated exactly to
the world SI standard meter. If the ruler is made of metal, and the room
temperature is not the same as the temperature at which the ruler was
calibrated, each centimeter will be slightly longer or shorter than the
calibration. These errors are called systematic errors because every
measurement that you take with that ruler will contain the same error. In fact,
you would not even know that your ruler was a source of systematic error until
you compared it with another ruler calibrated exactly to the SI standard
meter. Systematic errors are often much harder to recognize than random errors
and can be more harmful to the final result.

Error Control
The scientists and engineers behind MAP
have developed several methods that will help eliminate random and systematic
errors in MAP's data:
- Symmetry
- The structure of MAP is highly
symmetrical. This reduces the sources of error because it allows MAP to
measure the CMB anisotropy
using two different reflectors from the same position in space. This reduces
both the random and systematic error.
- Anisotropy
- The fact that MAP measures the CMB anisotropy rather than the absolute
temperature values of the CMB is a major factor in reducing error. For a more
detailed explanation of why this is the case, see
The Educator's Page.
- Frequency Measurements
- MAP collects data at five different frequencies in the microwave band of
the electromagnetic spectrum. These are: 22 GHz, 30 GHz, 40 GHz, 60 GHz, and 90
GHz. This has the effect of allowing MAP scientists to eliminate the galactic
foreground signal from the data.
- Repeated Observations of the Sky
- MAP is designed to observe a large fraction of the sky every day.
Over the course of a year, each piece of the sky will be
reobserved several thousand times, thus reducing the effects
of a one-time random error contaminating several data points.
- Spin Angle
- The angle between the spacecraft's spin axis and the sun is kept constant
in order to maintain a stable illumination of MAP's solar panels, providing a
thermally stable environment. This in turn reduces the systematic effects.

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