Definition of a Type I Error
A Type I error occurs in hypothesis testing when a researcher incorrectly rejects a true null hypothesis. It is often referred to as a "false positive" because the test concludes there is an effect or relationship when, in reality, there isn't one.
Understanding the Null Hypothesis and Alpha Level
To grasp a Type I error, one must understand the null hypothesis (H0), which states there is no effect or no difference. The probability of committing a Type I error is denoted by α (alpha), also known as the significance level. Commonly, α is set to 0.05, meaning there's a 5% chance of making this error.
A Practical Example of a Type I Error
Imagine a pharmaceutical company testing a new drug. The null hypothesis is that the drug has no effect. If the company concludes the drug is effective (rejects the null hypothesis) when it actually isn't, they've made a Type I error. This could lead to a useless or potentially harmful drug being approved for market.
Consequences and Control of Type I Errors
The consequences of a Type I error can range from wasted resources (e.g., investing in an ineffective drug) to severe harm (e.g., misdiagnosing a healthy patient with a disease). Researchers control the probability of a Type I error by carefully choosing the significance level (α) before conducting the experiment, balancing the risk of false positives against false negatives (Type II errors).