In this topic we are learning to: z Distinguish between random and systematic errors. z Reflect on how random and systematic errors can be reduced.
We measure physical quantities to collect a set of numbers for the unit being measured. When we have a large amount of these numbers, we have collected a set of data.
We collect sets of data for physical quantities for the following reasons: 1. To gather evidence that can be used to test a hypothesis.
2. To get numerical evidence to explain why something works the way it does.
3. To find patterns and relationships between the physical quantities being measured.
4. To find out when the data does not turn out the way we expect it to.
It is very important that the data we collect is accurate. If we do not measure physical quantities in the correct way, we end up with inaccurate data. When this happens, it is known as an error in the measurement.
There are two main sources of errors when taking measurements: random errors and systematic errors.
Fig. 15.4.1 A graph showing outliers B Random Errors
Random errors cause specific readings to be too high or too low.
Random errors are caused by taking a measurement the wrong way or by a difference in the reaction times of two people taking a measurement.
They often happen when a large number of readings have been taken and only one or two of these readings do not fit with the others. To avoid random errors, an average (or mean) of the data should be taken.
A
When compared to the rest of the readings taken, a random error will stand out as an outlier.
For example, in the graph in Fig. 15.4.1 the data collected at A and B are outliers.
Spotting random errors
Make a list of the types of random errors that could occur: a. When you measure the length of a curved line. b. When you are reading the volume of a liquid from a graduated cylinder.
