How Science works : Errors

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How Science works Errors
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Learning Objectives You should learn

• About different types of errors,
• How to reduce them when you are doing your
  practical work.

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What is an error?
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Types of errors
When reading scales, there are 2 main types of
error

• Random errors
• Systematic errors.

Lets look at some examples . . .
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Random errors

• These may be due to
• human error
• a faulty technique
• or faulty equipment.

To reduce the error, take a lot of readings, and
then calculate the average (mean).
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Human error
Example
Reading a burette
2.45ml or 3.55ml?
Correct volume 2.45ml
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Faulty technique
Example 1
Measuring a volume
Which size measuring cylinder should you use to
measure 20ml?
Answer 25ml
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Faulty technique
Example 2
Reading a burette
Do you look from above the meniscus?
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Faulty technique
Example 2
Do you look from below the meniscus?
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Faulty technique
Example 2
Or do you look level with the meniscus?
Answer level
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Faulty equipment
Example
Which thermometer is correct?
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Systematic errors
These errors cause readings to be shifted one
way (or the other) from the true reading.
Your results will be systematically wrong.
Lets look at some examples . . .
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Systematic errors
Example 1 Suppose you are measuring with a ruler
If the ruler is wrongly calibrated, or if it
expands,
then all the readings will be too low (or all too
high)
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Systematic errors
Example 2 If you have a parallax error
with your eye always too high
then you will get a systematic error
All your readings will be too high.
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Systematic errors
A particular type of systematic error is called a
zero error.
Here is an example . . .
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Zero errors
Example Look at this top-pan balance
It has a zero error.
There is nothing on it, but it is not reading
zero.
What effect do you think this will have on all
the readings?
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Anomalous results
When you are doing your practical work, you may
get an odd or inconsistent or anomalous
reading. This may be due to a simple mistake in
reading a scale. The best way to identify an
anomalous result is to draw a graph. For example
. . .
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Anomalous results
Look at this graph
Which result do you think may be anomalous?
A result like this should be taken again, to
check it.
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In summary

• Human errors can be due to faulty technique.

• Parallax errors can be avoided.

• Anomalous results can be seen on a graph.

• Random errors can be reduced by taking many
  readings, and then calculating the average (mean).

• Systematic errors, including zero errors, will
  cause all your results to be wrong.

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Learning Outcomes You should now

• Understand the effects of
• - Human error, including parallax error,
• - Random errors,
• - Systematic errors, including zero errors
• Be able to reduce these errors when doing your
  practical work
• Be able to identify anomalous results.