Glossary of scientific terminology relating to practical investigations


There are a couple of 'sing-along' activities for you to use with this topic. I wrote them at Christmas time - but the words are useful for any time of year!


There is also a set of revision flashcards to help you learn this topic


An accurate measurement is one which is close to the true value.  


This involves fixing known points and then marking a scale on a measuring instrument, between these fixed points.  


This refers to a collection of measurements (quantitative data) or observations (qualitative data).   For example: Data can be collected for the volume of a gas or the type of rubber.


The singular of data.  


- random These cause readings to be different from the true value. Random errors may be detected and compensated for by taking a large number of readings and repeating readings. You can then eliminate anomalies and average results. For example: Random errors may be caused by human error, a faulty technique in taking the measurements, or by faulty equipment.


- systematic

These cause readings to be spread about some value other than the true value; in other words, all the readings are shifted one way or the other way from the true value.


For example: A systematic error occurs when using a wrongly calibrated instrument.


- zero These are a type of systematic error. They are caused by measuring instruments that have a false zero. For example: A zero error occurs when the needle on an ammeter fails to return to zero when no current flows, or when a top-pan balance shows a reading when
there is nothing placed on the pan.


This comprises data which have been subjected to some form of validation. It is possible to give a measure of importance to data which has been validated when coming to an overall judgement.  

Fair test

A fair test is one in which only the independent variable has been allowed to affect the dependent variable.Care has to be taken to keep all but the independent and dependent variables constant. For example: A fair test can usually be achieved by keeping all other variables constant.


The precision of a measurement is determined by the limits of the scale on the instrument being used. Precision is related to the smallest scale division on the measuring instrument that you are using.
It may be the case that a set of precise measurements has very little spread about the mean value.
For example: using a ruler with a millimetre scale on it to measure the thickness of
a book will give greater precision than using a ruler that is only marked in centimetres.

Reliability and Repeatablity

The results of an investigation were considered 'reliable' by AQA if the results could be repeated. After much discussion this term was replaced (quite rightly in my opinion) with repeatablity. If someone else can carry out your investigation and get the same results as you, then your results are more likely to be reliable and therefore 'repeatability' and 'relilability are linked.
One way of checking reliability is to compare your results with those of others and/or quoted values - you being able to repeat them is not enough.

The repeatability of data can be improved by carrying out repeat measurements and calculating a mean. If the spread of results about that mean is large then the presicion is poor (see above).


True Value

This is the accurate value which would be found if the quantity could be measured without any errors at all.  


Data is only valid for use in coming to a conclusion if the measurements taken are affected by a single independent variable only. Data is not valid if, for example, a fair test is not carried out or there is observer bias. For example: In an investigation to find the effect on the rate of a reaction when the concentration of the acid is changed, it is important that concentration is the only independent variable. If, during the investigation, the temperature also increased as you increased the concentration, this would also have an effect on your results and the data would no longer be valid


- categoric A categoric variable has values which are described by labels.
When you present the result of an investigation like this, you should not plot the results on a line graph; you must use a bar chart or pie chart.

For example: If you investigate the effect of acid on different metals, eg copper, zinc and iron, the type of metal you are using is a categoric variable.


- continuous A continuous variable is one which can have any numerical value. When you present the result of an investigation like this you should use a line graph. For example: If you investigate the effect on the resistance of changing the length of
a wire, the length of a wire you are using is a continuous variable since it could have any length you choose.


- control A control variable is one which may, in addition to the independent variable, affect the outcome of the investigation. This means that you should keep these variables constant; otherwise it may not be a fair test. If it is impossible to keep it constant, you should at least monitor it; in this way you will be able to see if it changes and you may be able to decide whether it has affected the outcome of the experiment.  


- dependent and
independent variables
Often in science we are looking at 'cause and effect'. You can think of the independent variable as being the 'cause' and the dependent variable as being the 'effect'. In other words, the dependent variable is the thing that changes as a result of you changing something else.  


- dependent The dependent variable is the variable the value of which you measure for each and every change in the independent variable.  You usually plot this on the Y-axis of a graph


- independent


The independent variable is the variable for which values are changed or selected by the investigator. In other word, this is the thing that you deliberately change to see what effect it has.  You usually plot this on the X-axis of a graph


- discrete


You may sometimes come across this term. It is a type of categoric variable whose values are restricted to whole numbers. In other words you cannot have a 'half' of one of these. For example, the number of carbon atoms in a chain.

(You can't have half an atom!)


At A-level you get discrete ionisation energies for electron jumps within atoms - these are not 'whole' numbers but are discrete (you can't have any values between them - but at GCSE the discrete means 'whole'


- ordered

You may sometimes come across this term. It is a type of categoric variable that can be ranked.

For example, the size of marble chips could be described as large, medium or small.