What Is a Type II Error? (Importance, Example, and Tips)

By Indeed Editorial Team

Published June 2, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Some businesses collect and use data daily to make decisions about customers, products, services, and company operations. They often employ researchers who make and test hypotheses using this statistical data that they then accept or reject, which can sometimes result in errors. As a researcher, analyst, or someone pursuing a statistics-based profession, it's critical to understand how to read and interpret data used in hypothesis testing.

In this article, we define a type II error, reveal its significance, compare type I and II errors, discuss the rate of error, offer tips for reducing errors, and review an example.

What is a type II error?

In statistics, when a researcher tests an assumption or hypothesis, they can inadvertently cause a type II error. This error, sometimes referred to as a beta error (β) or error of the second kind, can occur when a researcher accepts a null hypothesis that's actually false. The null hypothesis is the starting point or base position upon which researchers test their results to find statistical significance between the variables.

It's the assumption there's no statistical relationship or effect between the variables being tested, so by testing it, the researcher can estimate the probability that an association results from chance.

A null hypothesis may be true or false depending on the statistical significance of the data, which means whether the data is useful as a measurement to disprove the hypothesis. When researchers don't reject a false null hypothesis, it can cause a false negative when a factor they consider unrelated actually affects the result. This is also called an error of omission because the researcher isn't excluding this unreliable data. Researchers typically treat a null hypothesis as if it's true until data disproves it.

Related: Types of Variables in Statistics and Research (With FAQs)

Why are these types of errors significant?

These errors can have a considerable impact on research results because they mean a null hypothesis is actually false when the researcher believes it's true. This error can mean researchers might miss significant opportunities to create innovative products or make basic improvements to processes or systems that may have a real impact on people.

When testing hypotheses, if you think you've encountered this type of error in your research, you can reexamine the data and null hypothesis. This step can help ensure you've included all the important data in your decision to accept the hypothesis and possibly prevent unnecessary outcomes.

The following are some examples of how recognizing and fixing beta errors can have a positive and significant effect on various businesses or situations:

  • A pharmaceutical company produces a useful medication that first appeared unusable and can now offer an alternative medication at a lower cost.

  • A startup company creates an effective marketing campaign for an essential community service that first appeared ineffective.

  • An electric car company removes unfavourable advertisements that first appeared favourable and reaches a new market segment increasing sales.

  • A medical clinic provides helpful medical services that first appeared unhelpful and patient complaints go down.

  • A clothing factory changes unsuccessful management methods that first appeared successful and worker morale and productivity improve.

Related: 12 Jobs for Statistics Majors (With Salaries and Duties)

Comparing type I and II errors

In statistical analysis, there's also a possibility of getting a type I or alpha error (α). This error occurs when the researcher rejects a true null hypothesis, unlike when they neglect to reject a false null hypothesis, causing beta errors. In either case, researchers end up confirming a wrong idea. When the null hypothesis is really true and researchers reject it, it can create a false positive. Type I errors reject the H1 even though it isn't a chance occurrence as determined by the sample.

Type I errors are inversely proportional to beta errors, so if you overcorrect for one type of error, you may inadvertently create the other type. The following chart may help you better understand the concept of these errors and their relationship to the null hypothesis:

Null hypothesis is:TrueFalse
DecisionReject null hypothesisType I errors (false positive)Correct decision (true positive)
Fail to reject null hypothesisCorrect decision (true negative)Type II errors (false negative)

While both errors can cause researchers to overlook important information, the biggest difference between type I and II errors is how they're created. For example, a type I error often happens if researchers accept random or coincidental data as statistically significant and decide to reject the null hypothesis based on that information. Often, this means your set significance level, which is typically around 0.05 or 5%, is too high. For beta errors, the error can happen if you overlook significant data because of a small sample size, low significance level, or measurement mistake. The key points to remember are:

  • type I errors happen when researchers wrongly reject a true null hypothesis and create a false positive

  • beta errors happen when you wrongly accept a false null hypothesis and create a false negative

  • both result in a belief that the findings are significant and have resulted from chance

Related: How to Find Critical Value in Two Steps (With Examples)

Rate of error in hypothesis testing

Statistics researchers measure relationships or occupances of specified variables in terms of their likelihood or probability of being related to the hypothesis. They note probability as the p-value and express this as a number between 0 and 1 that shows whether the feasibility of the variables tested has an effect. As a result, they assume this data occurred randomly by chance and the null hypothesis is true. The smaller the p-value, the stronger the evidence to reject the null hypothesis. When a p-value is less than 0.05, which is expressed as ≤ 0.05, researchers consider this to be statistically significant.

This means the null hypothesis is likely wrong and results are random because probability of H0 being right is only 5%, so researchers might consider accepting H1. Conversely, p-values over 0.05 don't reveal statistical significance and show strong evidence of no relationship between variables, so H0 is correct. Researchers can then keep the null hypothesis, rejecting the H1. Perfect tests show 0% false positives or false negatives. Accepting or rejecting H0 doesn't prove it's correct because there's never 100% certainty in hypothesis testing, as it isn't feasible to test 100% of a population.

Tips for reducing errors

Researchers note chances of committing type I errors as alpha (α). Researchers use the term statistical power to mean the probability the hypothesis test can accurately reject H0, so power is the degree to which a test accurately detects a true effect. To reduce the chances of creating errors in hypothesis testing, here are some tips to consider:

  • Plan studies carefully: Thoughtful planning can help you avoid errors by ensuring you have all the data to make reasonable decisions about a null hypothesis.

  • Prepare your data: The more you understand the variables in your statistics, the more you can ensure external factors don't influence the data and you have included all key information that might be significant to the results.

  • Increase your sample size: Typically, increasing sample size is more representative of a true population and helps eliminate outliers that can negatively affect the outcomes of a study.

  • Run longer tests: Running tests for longer durations can help ensure the data is consistent and not attributed to sudden spikes or falls in activity, which can allow you to make more meaningful decisions.

  • Raise significance level: Increasing the significance level higher than 5% means you can consider more data significant and increase sample size. Raising the significance level too high can create type I errors, so you can perform multiple tests at different levels and compare data with other researchers.

Related: 18 Data Analyst Skills for Success

Example of a beta error

An easy way to understand the concept of a null hypothesis is to think of it as the no difference assumption or hypothesis. Researchers label it as H0 to represent the baseline hypothesis for testing the statistical significance of variables, so it becomes hypothesis zero. The alternate hypothesis (H1) tries to prove there's a relationship or association between two variables, so the opposite of what H0 tries to prove. When researchers attribute wrong assumptions to H0, they get beta errors. The following example may help you better understand this error and how it can occur:

Example: A bank wants to increase customer satisfaction by extending hours of operation. Your null hypothesis is that increasing opening hours won't increase customer satisfaction. To test this, you give customers a satisfaction survey after each in-person transaction, asking for their opinions on the extended hours. After a month, the surveys suggest no change, so you accept your null hypothesis that increasing opening hours doesn't increase customer satisfaction. The bank continues to offer the survey, and after three months, customer satisfaction levels have risen. You realize you accepted a false hypothesis without having enough information to make an accurate decision.

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