Data and Dependent Variables

A recap of the basics

You have already covered the basics of statistics last year. So, it’s worth a recap now, to understand what you have already done. If you do understand last year's foundations, then it makes the rest of the process much easier.

Data

Data are pieces of information. In most science, data are normally numbers. When you collect information (data), it is worth seeing if you can make it a meaningful number. Sometimes this is easy:

Length (measured in metres),

Mass (measured in kg),

Number of species (as a direct count),

Percentage cover of plants in a quadrat.

But sometimes it may take more thought. However, it can often be possible to transform lots of types of data into numbers.

Examples:

Immune response → white blood cell count,

darkness of rocks → transform a photo to greyscale and then pick out pixel values,

amount of photosynthetic bacteria → chlorophyll a absorption levels from a spectrophotometer

Normally these numbers that you measure are called ‘dependent variables’. They depend on something else (an independent variable). So, for example – immune response might depend on exposure to disease. Darkness of rock may depend on distance to a pollution source.

The dependent variable is (almost) always a number. If you were to plot a graph, the dependent variable would go on the y (or vertical) axis.

Whenever you plan work, such as your third year project, think very carefully about what you can measure. If you want to measure something which isn’t an obvious number – then look at other studies. Even if you don’t understand all of their complicated statistics, the odds are, they will have found a way to do convert what they want to measure into a number.

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