Liam Healy & Associates

chartered occupational psychologists

Research Design and Statistical Consultancy

Statistics is the science of generalising from a small sample to a wider population. Anyone who has ever tried to summarise and draw conclusions from data will appreciate how difficult it can be. All of our Psychologists are Chartered Scientists, and good statistical practice is part of our everyday work,  but we also provide a specialist research consultancy service to clients.

Much of the data analysis we see contains common errors. The commonest error is failing to apply a hypothesis driven analysis, and using a grossly oversimplified (and often completely incorrect) analysis model. Here are a few of the more common mistakes we come across :

  • Confusing descriptive and inferential data.
  • Trying to draw conclusions and make predictions based on descriptive analysis only.
  • Confusing data types - nominal. ordinal, interval and ratio, and hence using the wrong summary statistics.
  • Failing to understand or use a proper hypothesis testing approach, relying instead on a 'fishing trip' approach.
  • Drawing unfounded conclusions from observational data.
  • Not understanding the technical definition  of significant, and failing to apply any statistical technique to test it.
  • Making unwarranted extrapolation from weak data.
  • Not being aware of, or understanding Type I or Type II errors.
  • Using correlation to infer a causal relationship.
  • Confusing standardised and raw data types.
  • Confusing the nature of, and relationship between, dependent and independent variables.
  • Failing to account for measurement error and failing to calculate basic descriptive values such as  the Standard Error of the Mean.
  • Failing to define analysis as being 'between subjects' vs. 'within subjects', and hence being unable to choose the correct procedure for analysis. 
  • Not understanding that observed differences have to be statistically significant, instead relying on gut-instinct, or 'eyeball' analysis to conclude there is a difference, when in fact the data does not support this.
  • Poor quality sampling - non-randomised, non-stratified, too small, or unrepresentative.
  • Failing to adopt good research practices such as blinding and controlling for variables.
  • Not applying Confidence Intervals, or failing to understand how they affect interpretation of results.
  • Carrying out analysis with severely range restricted data.

There are a lot more but we'll stop the list there!

If you are carrying out analysis of crucial business information, and will be making decisions based on the results, we will be happy to help you ensure your research and analysis model is the correct one, and that you can have faith in the results.

We can usually provide this service remotely.

We also offer a bespoke Statistics for Business Training Workshop designed for people who need a grounding in basic statistical concepts and procedures. Please contact us for more details.