Practical Skills
Planning
Variables
Often an experiment involves things that can change, known as variables. Variables need to be identified, so they can then either be changed or controlled. There are three kinds of variable:
dependent
independent
control
Scientists often want to find out if changing one variable makes a difference to other variables. In many (though not all) investigations the variables are kept constant - the control variables, apart from one which is varied - the independent variable. The effects of the independent variable is then determined by monitoring the dependent variable.
Values and readings
The values are the measurements used for the independent variable. If, for example, one of the variables in an experiment was length, it would be important to decide the maximum and minimum values, and also the intervals between values. If enzyme activity at different pH values was being investigated, a decision would have to be made on what values of pH to use. This decision would take into account elements such as available equipment, time constraints, and safety.
When measurements are being taken, it is usually appropriate to repeat them. Sometimes, there are lots of possible readings that could be taken. For example if the distribution of daisies on a playing field was being explored, it wouldn’t be necessary to count every one; however, it wouldn’t be a good idea to just look at the ones close to the fence. A sampling technique should be used to decide which ones to look at. It might, for example, involve the method of randomly placing quadrats. A mean is then calculated.
Equipment
If certain chemicals are going to be used, the potential hazards need to be identified to ensure that they’re used safely. This might affect the concentrations of solutions or the quantities used and even whether those substances are used at all. The hazards also need to influence the general running of the experiment and how the equipment is used.
The next step is to think about the most appropriate equipment to use. For example, the volume of a liquid could be measured using a beaker, a measuring cylinder or a burette. In different circumstances one of these might be safer or more accurate than others, which would affect the choice.
If you need to measure out 5 cm3 of liquid then a 10 cm3 measuring cylinder would give a more accurate volume then using a 100 cm3 measuring cylinder. Also, using balances that measure mass to the nearest 0.01 g will give a more accurate measurement then using ones that measure mass to the nearest gram.
Observation
The experiment should be conducted in a clear and systematic way to ensure the data is complete and of a high quality. In an experiment into the relationship between force, mass and acceleration a toy car of different masses runs down a ramp. The acceleration needs to be measured several times at each mass. The repeats would all need to be done in the same way and with care to ensure precise data. If you observe that a repeat is not similar to the others then it is good idea to repeat it.
important to pay careful attention while the experiment is being carried out. It might be that the car starts to deviate from a straight line path; which if significant may mean that the method should be modified.
Analysing
Studying the data
Data collected during an investigation is normally displayed in a results table. At this point you can study your repeats to see how close they are. Repeats that are similar are said to be precise. Sometimes you may have an anomalous repeat. If this is the result of a measurement error it can be ignored, although it is good practice to repeat that measurement again.
How to display the data
It can be difficult to see the relationship between the variables from a results table so often the means are plotted on a graph or chart to analyse the results further. It is important to choose the most appropriate type of graph or chart.
When scientists start to investigate something they usually have a hypothesis that they are testing. This means they have an idea about what will happen when they explore something or take some readings, but they need the evidence to either confirm their thinking or suggest they need to think again.
From this they can make a prediction. It is easy to get mixed up between hypotheses and predictions. For example, a hypothesis might be made about the way that springs behave when they are loaded. From this a prediction can be made about what will happen to a spring when force is added.
Evaluation
The final stage is to consider what has been learned from the investigation and the quality of the data. If it is decided that the experiment could have been improved in some way; suggestions should be considered of how and why.
Drawing conclusions
In this part you will say what your results show, and how this relates to the prediction you made at the start of the investigation.
Evaluating data
You need to consider if the data is of high quality. As well as looking at precision of the results, you can also consider repeatability and reproducibility.
Results are said to be repeatable if similar results are obtained when you repeat your investigation. To check reproducibility, you need to get someone else to follow your method and see if their results are similar to yours.
If the data is considered to not be of high quality then the method used might not be suitable.
Suggesting improvements
How accurate were your results? If there are sources of error then they will not be close to the true value and so not accurate. There are different sources of error:
Random errors are due to things you have no control over, such as a change in room temperature whilst you were collecting the results. Repeating your measurements and finding a mean will reduce the effect of random errors.
Systematic errors are due to problems with the equipment you used. For example, the balances you used may have been out by 0.1 g for every measurement.