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How Science works: Errors

How Science works: Errors. Learning Objectives You should learn : . About different types of errors, How to reduce them when you are doing your practical work. An error is a mistake of some kind. …so the result is not accurate. …causing an error in your results…. What is an error?.

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How Science works: Errors

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  1. How Science works: Errors

  2. Learning Objectives You should learn : • About different types of errors, • How to reduce them when you are doing your practical work.

  3. An erroris a mistakeof some kind... …so the result is not accurate. …causing an error in your results… What is an error?

  4. Some are due to human error… What is an error? For example, by not using the equipment correctly Let’s look at some examples.

  5. Human error Example 1 Professor 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?

  6. Human error Answers: • Measuring from 100 end • 95.4 is the wrong number • ‘mm’ is wrong unit (cm) • Hand-held object, wobbling • Gap between object & the rule • End of object not at the end of the rule • Eye is not at the end of the object (parallax) • He is on wrong side of the rule to see scale. How many did you find?

  7. Human error Example 2 youreye Reading a scale: Discuss the best position to put your eye.

  8. Human error youreye 2 is best. 1 and 3 give the wrong readings. This is called a parallax error. It is due to the gap here, between the pointer and the scale. Should the gap be wide or narrow?

  9. 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 . . .

  10. x x x x x x 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.

  11. Types of errors When reading scales, there are 2 main types of error: • Random errors • Systematic errors. Let’s look at some examples . . .

  12. 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).

  13. Systematic errors These errors cause readings to be shifted one way (or the other) from the true reading. Your results will be systematically wrong. Let’s look at some examples . . .

  14. 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):

  15. 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.

  16. Systematic errors A particular type of systematic error is called a zero error. Here are some examples . . .

  17. Zero errors Example 3 A spring balance: 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?

  18. Zero errors Example 4 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?

  19. Zero errors Example 5 Look at this ammeter: If you used it like this, what effect would it have on your results?

  20. Zero errors Example 6 Look at this voltmeter: What is the first thing to do? Use a screwdriver here to adjust the pointer.

  21. Zero errors Example 7 Look at this ammeter: What can you say? Is it a zero error? Or is it parallax?

  22. Zero error, Parallax error Example 8 Look at this ammeter: It has a mirror behind the pointer, near the scale. What is it for? How can you use it to stop parallax error? When the image of the pointer in the mirror is hidden by the pointer itself, then you are looking at 90o, with no parallax.

  23. 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.

  24. 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.

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