If you will be using the data to present an argument to someone, whether that is face-to-face or in writing, you will want to interpret the data in the way that best supports your argument. Here are a some tips and tricks on interpreting the data in different ways.

1- 1% seems like quite a small number doesn’t it? But remember that in your survey, 1% might represent the total population of the United States, for example. Most companies would be very happy to be able to sell their product or service to 1% of the US – that’s around three million people.

2- When using ‘scale’ answers (e.g. completely satisfied, fairly satisfied, not very satisfied, not at all satisfied), people tend to add together the positives (using the example above, ‘completely satisfied’ plus ‘fairly satisfied’), and then just look at that single number. But that can mask important discoveries. For example:

Q. How satisfied are you with your television?


In the example above, by adding the two positive responses together, it shows that 73% are satisfied (18% + 55%). This shows that most people are quite happy with their television. But there is a bit more to the story: most are happy with their television to some extent, but the vast majority are unhappy with something about their television, as only a minority (18%) are completely satisfied.

3- At its most basic level, data analysis is comparing two numbers with each other and explaining why they are different, or why they are similar. One of the easiest types of analysis is when you are comparing the data for two identical answers, at two different periods of time. This is because it creates a story that is very easy to understand.
For example, if you measure in March that around 10% of people have recently bought a new TV, and then in October you ask the same question to find out that 20% of people have recently bought a TV, then it is immediately obvious to everyone looking at the data that more people are buying TVs.
So if you are trying to think of carrying out some research to quote in a blog or advertising, but are struggling for new topic areas, consider re-running research that you have done before. You might find it even more interesting and easy to use than you did the first time!