Type I Error
What is a Type I Error?
A type I error occurs when a hypothesis test incorrectly rejects a true null hypothesis. This is commonly known as a "false positive," meaning the test suggests that there is an effect or difference when, in reality, none exists. The probability of making a type I error is denoted by alpha (α), often set at 0.05, representing a 5% chance of incorrectly rejecting the null hypothesis.
The Basic Idea
Imagine you are conducting a test to see whether using a fertilizer will improve plant growth in your garden. In an experiment, you always begin with a null hypothesis, which suggests that there is no statistical relationship between the variable (using the new fertilizer) and the outcome (plant growth), and an alternative hypothesis, which states that the variable does have a significant impact on the outcome.1
To conduct your experiment, you use fertilizer for one of your lilac shrubs and leave the other shrub without. After a month, you measure the growth of each lilac shrub and find that the fertilized lilac has grown bigger than the unfertilized one. You assume that the fertilizer significantly improves plant growth and reject the null hypothesis.
However, later on, when you start using the fertilizer for all your plants, you realize that it does not seem to be causing improved growth. You realize that the reason the first lilac shrub grew bigger was actually because it was closer to the window and received more sunlight.
In this instance, you have made a type I error: a false positive conclusion. Type I errors occur because of random chance (which is more likely to happen with a small sample size, such as comparing only two lilac bushes) or improper testing techniques (not controlling other variables like sunlight or concluding the experiment too early).2
Type I errors cause people to conclude results are statistically significant when, in reality, they are not.3 Imagine that you started telling your friends about the fertilizer and its impact on growth, only for them to waste their money on it and see no results—you may have a lot of annoyed friends! While in this scenario, the consequences of the type I error are not too serious, they can have far-reaching consequences in medical research. If a researcher concludes that a drug improves patient outcomes, it would pass to market and be recommended for treatment. In this case, the false conclusion about an ineffective medication could be a matter of life or death.
One of the most common as well as most important problems which arise in the interpretation of statistical results is that of deciding whether or not a particular sample may be judged as likely to have been randomly drawn from a certain population, whose form may be either completely or only partially specified.
— Jerzy Neaman and Egon Pearson, in their 1928 paper “On The Use and Interpretation of Certain Test Criteria for Purposes of Statistical Inference: Part I.” 4
About the Author
Emilie Rose Jones
Emilie currently works in Marketing & Communications for a non-profit organization based in Toronto, Ontario. She completed her Masters of English Literature at UBC in 2021, where she focused on Indigenous and Canadian Literature. Emilie has a passion for writing and behavioural psychology and is always looking for opportunities to make knowledge more accessible.
