Key/Independent Variable

When you write out your hypothesis, most people will naturally place the key independent variable first. For example, "I think that as the light intensity is increased, the seedlings will grow taller because ...". It could have also been written as I think that the seedlings will grow taller as the light intensity increases because ..." so it doesn't happen all the time. When deciding on the key independent variable, the most reliable way of working out what it is, is by checking your hypothesis for the variable that you think will cause something else to change in a predictable way. In our example, you can see that the light intensity is the key independent variable because it is the one that you think will cause the seedlings to grow taller.

As with all variables, the data can be continuous or discrete. Continuous data can have any value and so you must decide on the range and number of readings that you will take. You must consider how many readings you think are practical, for instance, if you have temperature as the key independent variable, in a school laboratory, -5^oC would be the lowest practical temperature you could achieve (using a mixture of water, ice and salt) and would you really need to take readings at 1^oC intervals? Of course not! Every 5 or 10 degrees would give you sufficient results from which to draw a conclusion. You also need to take into account safety factors, so using concentrated hydrochloric acid would not be possible as it is not allowed to be used in middle school science experiments because it is dangerously corrosive. You are more limited in your choices if your key independent variable has discrete values, but you will probably still need to make decisions based on practicality and safety.