What's the difference between Type I vs. Type II errors in statistics?

Imagine you’re a doctor reviewing test results. You have to decide: does the patient have a disease or not? You choose yes, but later, it turns out they're completely healthy. Oops! That’s a Type I error — a false alarm.

Now imagine the opposite: you say the patient is fine, but later, they develop serious symptoms because you missed the diagnosis. That’s a Type II error — a missed opportunity to act.

These two types of errors pop up everywhere, from medical tests to business decisions and even courtroom verdicts. Let’s break down the difference between Type I vs. Type II errors in statistics.

Before we dive into understanding Type I vs. Type II, it's important to understand null hypothesis. Null hypothesis is what is assumed to be true.

At the end of the day, a hypothesis is an educated guess, meaning it's not always true. This is why we conduct surveys and tests to verify it.

In an ideal world, we can ask everyone and collect all the data we need. But realistically, researchers rely on sample sizes. The smaller the sample size, it's likely that there will be a faulty result.

Back to the hospital example, a medicine might work for some patients. However, some may be allergic to the medication. We need to test to make sure it's safe.

A Type I error happens when we reject a null hypothesis, a hypothesis that is in fact true. In simple terms, it’s when you think something is happening when it actually isn’t.

Example:

• A pregnancy test wrongly says someone is pregnant when they’re not.

• A fire alarm goes off when there’s no fire.

In statistics, professionals use numerous tests such as "t-statistics" or "chi-square" to determine how likely a conclusion will be a Type I error (a false alarm). Typically, a value below 5% can be considered acceptable for general research, but not for medical drugs

Which of the following situations describe a Type I error (false positive)?

A. A bank’s fraud detection system mistakenly flags a legitimate transaction as fraud and blocks the customer’s credit card.

B. A self-driving car’s system wrongly identifies a plastic bag on the road as a large obstacle and slams the brakes.

C. A job recruiter ignores a highly qualified candidate, thinking they wouldn’t be a good fit when they actually would have been perfect.

D. A professor incorrectly accuses a student of cheating on a test when they didn’t.

A Type II error happens when we fail to reject a false hypothesis. This means we miss something that’s actually there.

Example:

• A test fails to detect a disease when the person actually has it.

• A security scanner at the airport misses a hidden weapon.

Researchers may commonly use the Statistical Power Test to see how likely a conclusion will be a Type II error. This is important because missing a deadly disease could mean life or death in some situations.

Which of the following situations describe a Type II error (false negative)?

A. A medical test wrongly detects a disease in a healthy patient.

B. A smoke detector fails to go off even though there is a real fire.

C. A new plagiarism detection software fails to flag a clearly plagiarized essay.

D. A hiring manager overlooks a brilliant candidate, assuming they wouldn’t perform well in the role when they actually would have excelled.

The simplest way to remember the difference between Type I vs. Type II errors is:

• A Type I error (false positive) means detecting something that isn’t there —like an unnecessary panic or overreaction.

• A Type II error (false negative) means failing to detect something that is actually there — like overlooking an important warning sign.

• A Type I error is the alarm going off when there’s no fire.

• A Type II error is a fire breaking out, but the alarm doesn’t sound.

Think of Type I vs. Type II errors like fire alarms:

These errors matter because reducing one usually increases the other. If you make a test too sensitive, you’ll catch more real cases but also have more false positives. If you make a test less sensitive, you’ll avoid false alarms but risk missing real cases.

In any decision-making process, you want to minimize mistakes. But here’s the catch: reducing one type of error increases the other so it's important to make compromises.

• In medicine, we usually prefer to reduce Type II errors because missing a disease can be life-threatening. That’s why tests tend to err on the side of caution.

• In criminal justice, we want to minimize Type I errors — it’s worse to convict an innocent person than to let a guilty one go free.

• In business, a Type I error could mean wasting money on a bad idea, while a Type II error could mean missing out on a breakthrough opportunity.

In statistics, the likelihood of Type I error is referred to as alpha (α). If we set α at 5%, we're less likely to miss something that happens (Type II error).

But if we decide to set the test conservatively at 0.5% for α, we probably won't commit Type I error (false alarm). However, it also means we might miss something that's there.

Now that you've learned about the important difference between Type I vs. Type II errors, try out some activities!