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Data handlingHow accurate is a graph

It is important to make sure any information you work with is robust. Things to look out for include validity of the source, scale used, sample size, method of presentation and appropriateness of how the sample was selected.

Part of MathsData analysis

How accurate is a graph

Just because information is displayed in a graph doesn't mean that it is robust.

The websites Dog pals and CatMates both display graphs about whether dogs or cats make better pets.

Two phones display a bar chart each. Both show an increase but neither has any titles or labels. One says it shows dogs becoming the most popular pet. The other claims the same for cats.

When you look at any graph it is important to ask yourself three questions:

  • is this graph detailed?
  • is this graph accurate?
  • is this graph able to withstand close examination?

Looking carefully at these bar graphs we realise they don't tell us much at all. They show something going upward but we can't tell anything else:

  • there is no title - we can't tell what the graph is about
  • there are no axes or labels - we don't know what is being measured
  • there is no scale - so we don't know any of the numbers involved or how much they change
Two pie charts. One asked 1000 subscribers which was a better pet and shows dogs as the favourite. The other asked 500 subscribers which was a better pet and shows cats as the favourite

By comparison, these pie charts give us more detail, including the . Knowing how many people were surveyed by each website means we can decide if it was a big enough group to provide useful information. We can also calculate exactly how many people chose cats and how many chose dogs.

But...

Even though this piece of data looks more useful, we still need to consider the validity of their sources. This means assessing whether or not the websites could be biased. A good starting point is to look at the name of the companies.

In this case it is pretty straightforward: one website seems to be for cat-lovers and the other one for dog-lovers. They will very likely be biased!

Names can give you a clue as to who the source would favour (be biased towards). In many cases the websites people subscribe to will have views/opinions they already favour.

Outcome

Our assessment suggests that the data we found on those two websites is not robust enough. We would need to look elsewhere for more trustworthy information.