# RLO: Probability associated with inferential statistics

## Type 1 errors

Say we want to see if a group of patients, who have been given a new drug, have recovered more quickly than a group of patients who received the standard drug. We can use a statistical test to see if there is a difference. Whatever test we use we need to remember that the data we are analysing comes from groups that originally started off as similar to one another. If this were not the case we could not tell if the new drug had made the difference.

So if we find a difference, it might be due to the trial, but there is a possibility that it is due to sampling error. Another way of thinking about sampling errors is that it is the error that gives rise to the difference between the sets of data. If the error were not present then there would not be a difference. This type of sampling error, (known as a type 1 error ) says that a difference is found when no difference exists. It is one of the reasons why researchers publish the results of their research. This then enables other researchers to repeat the study to see if they find similar results. If the results were originally due to an error (which has a small chance of happening, ie less than 1 in 20, or 0.05) then repeating the study may not be able to reproduce the result.