How Science works:

Errors

• About different types of errors,• How to reduce them when you

are doing your practical work.

Learning ObjectivesYou should learn :

What is an error?An error

is a mistake of some kind...

…causing an error in

your results……so the

result is not accurate.

What is an error?

Some are due to human

error…

For example,by not using the equipment correctlyLet’s look at some examples.

Human errorExample 1Professor Messer is trying to measure the length of a piece of wood:Discuss what he is doing wrong.How many mistakes can you find? Six?

Human error

1. Measuring from 100 end2. 95.4 is the wrong number 3. ‘mm’ is wrong unit (cm)4. Hand-held object, wobbling5. Gap between object & the rule6. End of object not at the end of the rule7. Eye is not at the end of the object

(parallax)8. He is on wrong side of the rule to see

scale.

Answers:

How many did you find?

Human errorExample 2Reading a scale:

Discuss the best position to put your eye.

youreye

Human error

2 is best.1 and 3 give the wrong readings.This is called a parallax error.

youreye

It is due to the gap here, between the pointer and the scale.Should the gap be wide or narrow?

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 . . .

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.

xx

xx

xx

Types of errors

When reading scales,there are 2 main types of error:

Let’s look at some examples . . .

• Random errors• Systematic errors.

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).

These errors cause readings to be shifted one way (or the other) from the true reading.

Systematic errors

Your results will be systematically wrong.

Let’s look at some examples . . .

Example 1Suppose you are measuring with a ruler:

Systematic errors

If the ruler is wrongly calibrated, or if it expands,then all the readings will be too low (or all too high):

Example 2If you have a parallax error:

Systematic errors

with your eye always too highthen you will get a systematic errorAll your readings will be too high.

A particular type of systematic erroris called a zero error.

Systematic errors

Here are some examples . . .

Example 3A spring balance:

Zero errors

Over a period of time, the spring may weaken, and so the pointer does not point to zero:What effect does this have on all the readings?

Example 4Look at this top-pan balance:

Zero errors

There is nothing on it, but it is not reading zero.What effect do you think this will have on all the readings?

It has a zero error.

Example 5Look at this ammeter:

Zero errors

If you used it like this, what effect would it have on your results?

Example 6Look at this voltmeter:

Zero errors

What is the first thing to do?

Use a screwdriver here to adjust the pointer.

Example 7Look at this ammeter:

Zero errors

What can you say?

Is it a zero error?Or is it parallax?

Example 8Look at this ammeter:

Zero error, Parallax error

What is it for?How can you use it to stop parallax error?

It has a mirror behind the pointer, near the scale.

When the image of the pointer in the mirror is hidden by the pointer itself, then you are looking at 90o, with no parallax.

In summary• Human errors can be due to faulty

technique.

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

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

• Parallax errors can be avoided.• Anomalous results can be seen on a

graph.

• 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.

Learning OutcomesYou should now: