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Statistics in research

Statistical tests are fundamental to research, they allow us to quantify differences between sets of data and to be confident in our interpretation of results. Without statistics, many of the advances in medical research would not be possible!

When carrying out biological research, we often want to compare pieces of information to see if there is a difference between two sets of data. Sometimes you can tell the difference between data sets easily by eye (for example, the number of silver cars vs the number of yellow cars parked on a nearby street), but often the difference may be quite subtle. Either way, it is important to be able to quantitatively analyse this difference, so we can be confident in the interpretation of our results.

This is where statistical tests come in, they allow us to calculate how likely it is that the results we have gathered are due to chance. Statistics underpin research, and it is vital that scientists and researchers can use statistics appropriately and effectively. An example of this is in clinical trials, where statistical analysis allows scientists to be confident that patients taking the drug they are testing have a significant difference in a certain parameter (e.g. blood pressure) compared to patients taking a different drug, or a placebo.


This resource will help you to understand:

  • Basic terms of statistics, such as standard deviation, p-value and null hypothesis.
  • How to choose an appropriate statistical test for certain data sets.
  • How to use and interpret several statistical tests – the Student’s t-test, Spearman’s rank correlation coefficient and the chi-squared (χ2) test.
Statistics 1