A graph is a visual way to see patterns and trends in your data. You need to decide what value to put on your x-axis and y-axis and label both clearly. You also need to decide what units you intend to use to represent your values. Don’t forget to use conversion factors if units don’t match, for example cm and m.

There are a number of graphs you can choose from depending on your data:

• line
• bar
• histogram

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Bar charts

Bar charts can be used to look for difference between groups and a bar is plotted for each group being investigated. To calculate if the difference is real or due to chance spread values can be calculated. These values can then be plotted onto the chart as error bars. If there’s overlap of the error bars then the conclusion is that the difference is just random chance. No overlap, however, signifies that there’s a significant difference.

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Line graph

Once you’ve plotted a line graph you can see the correlation between the two variables. Your data can be:

• positively correlated: as one variable increases so does the other
• negatively correlated: as one variable increases the other decreases
• zero correlation: a change in values of one variable doesn’t affect the other

A causal relationship between two variables is where the occurrence of one variable causes the other. The first event is known as the cause while the second is known as the effect. Just because two variables are correlated doesn’t mean a causal relationship. However, if there is a casual relationship then this means they must be correlated.

You can also see if your data creates a straight line or a curve. If it’s a straight line the relationship is a linear relationship. If it’s a curve then it’s non-linear.

Other sets of data are neither. This is known as a scatter diagram and may require a line of best fit. This is a straight line running through the centre of the values so that it joins as many of the points as possible or at least keeps an equal number above and below the line. This way you can get a general direction of your data.

When you make a conclusion you need to relate it to the data collected. You can then use your understanding of the theory to explain why this conclusion was reached.