So you have a hypothesis and now you also have some data.

You will have looked at your data ad plotted it in some suitable way.

This will already, very likely, have given you an idea as to whether you are going to be able to reject your null hypothesis.

What type of test should you carry out to determine whether to reject the null hypothesis?

That depends on what type of data you have and how many you have collected.

We can use a simplified scheme for determining which test we need to use

The one below is taken from Barnard et al (2007), “Asking Questions in Biology”. The book contains both this and a more detailed version.

Some examples

Example One

You have carried out a survey that asked people their preference from among a range of enviroments. From a range of habitats, they had to select their favourite.

Suppose the results were:

  • Woodlands: 7
  • Beaches: 4
  • Mountains: 11
  • Deserts: 8
  • Oceans: 14

What kind of data are these: categorical, ordinal, constant interval or count?

  1. What might be a suitable null hypothesis in this case?

  2. Are we looking for a trend or a difference?

  3. How many groupings are there?

  4. How many treatments?

  5. Are data for each treatment replicated?

  6. Are data in the form of counts?

Example Two

You have gathered pain scores for 50 cats in post-operative care that had been offered analgesic A, and for 50 oothers that had been offered analgesic B. You want to find out if the pain is reduced for one analgesic compared to another.

What kind of data are these: categorical, ordinal, constant interval or count?

  1. What might be a suitable null hypothesis in this case?

  2. Are we looking for a trend or a difference?

  3. How many groupings are there?

  4. How many treatments?

  5. Are data for each treatment replicated?

  6. Are the data in one treatment independent of those in other treatments?

  7. Are the data normally distributed?

Example Three

You wish to see whether infra-red thermometers may be used instead of rectal thermometers to measure the body temperature of horses, since they are cheaper, quicker to use, less invasive and less distressing to the horse. To do this you need to compare a series of measurements made by each type of thermometer on 50 horses, to see whether the infra-red thermometer reliably gives the same reading as the rectoal thermometer.

For each horse, you record the readings of the two thermometers.

What kind of data are these: categorical, ordinal, constant interval or count?

  1. What might be a suitable null hypothesis in this case?

  2. Are we looking for a trend or a difference?

  3. How many groupings are there?

  4. How many treatments?

  5. Are data for each treatment replicated?

  6. Are the data in one treatment independent of those in other treatments?

  7. S_ay again, how many treatments were there?_