Once upon a time, a king decided to prove his kingdom’s superiority in archery.
So he challenged the neighboring kingdom to a competition, hoping to prove once and for all who’s the bravest of them all.
The King’s people were well-trained in archery. Yet, they lost.
The King made two fatal errors.
A Type 1 error (he assumed the competition would be fair) and a Type 2 error: he didn’t search the competition’s equipment for potential cheating.
Why the fairytale? Because we wanted to showcase the importance of understanding Type 1 and Type 2 errors in your decision-making process.
These errors can lead to incorrect conclusions, wasted resources, and even embarrassment (as in the King’s case).
This article delves into the technical nature of these errors, showing you how to avoid them in A/B testing. We’ll look at examples of both Type 1 and 2 errors, their consequences, and the factors leading to these errors.
Let’s ride!
What Is a Type 1 Error?
First and foremost, a Type 1 Error represents a statistical error that occurs when the researcher (or analyst) rejects a null hypothesis that is actually true.
It occurs when it’s concluded that there’s a significant effect or relationship between variables when, in reality, there is no such effect or relationship.
In statistics, Type 1 errors are also known as false positive results or simply false positives.
If you need help understanding Type 1 errors, look at this hypothetical.
Suppose someone is on trial for a crime.
In this scenario, the null hypothesis is that the person is innocent, and the alternative hypothesis is that the person is guilty.
The court’s decision to convict or acquit the person depends on the evidence presented in the trial.
Suppose the evidence presented in the trial cannot prove the person’s guilt.
Yet, the court still convicts the person due to some error or bias in decision-making. This happened because the court wrongly rejected the null hypothesis (that the person is innocent), deciding the person is guilty based on insufficient evidence.
A Type 1 error would mean the court mistakenly convicts an innocent person in this context.
Examples of a Type 1 Error
In a CRO scenario, you perform A/B tests for two variations of your web page: Version A and Version B.
The initial hypothesis is that there is no significant difference between the two versions, and the alternative hypothesis is that Version B provides a better conversion rate than Version A.
Suppose the significance level is 0.05 (i.e., a 5% chance of making a Type 1 error). In that case, the A/B test results show that Version B performs better than Version A with a p-value of 0.02 (less than the significance level).
In this case, you may mistakenly reject the null hypothesis and conclude that Version B is better than Version A when there is no significant difference between them.
This is a Type 1 error – even if the null hypothesis is true, you reject it.
Let’s think of a different example.
Suppose you’re a medical research professional testing a new drug for its effectiveness in treating a disease. The assumption is that the drug is ineffective, and the alternative hypothesis is that the drug is effective.
Suppose the significance level is set at 0.05, and the study results show that the drug is effective with a p-value of 0.02. In this scenario, the null hypothesis may be rejected, and the drug may be approved.
This is a Type 1 error because, in reality, the drug isn’t practical.
Why Do Type 1 Errors Happen?
Evidently, Type 1 Errors don’t happen intentionally; there’s no malicious intent behind them. So why do they appear?
Well, in some cases, these error types simply appear due to chance – call it bad luck. The analyst might reach a statistically significant result by chance despite no actual effect.
In other cases, Type 1 errors appear when there’s a sampling error, meaning the sample sizes used aren’t representative of the entire audience.
The results may not be generalizable when the sample size is too small or the sampling method is flawed.
Another situation favoring a Type 1 error is called P-Hacking.
P-Hacking means that the analyst intentionally chooses which statistical tests to report based on the results’ significance level. Consequently, significant findings are inflated, resulting in a Type 1 error.
Why Is It Important to Be Aware of Type 1 Errors?
You should be wary of every error in A/B testing because these errors will lead to incorrect conclusions, and your decision-making process will be based on flawed data.
For example, when a Type 1 error occurs, you may mistakenly conclude that one version of your webpage or app is better, implement the changes permanently, and lose the opportunity to turn traffic into paying customers.
Vigilance is crucial, as such errors can always occur.
Consequences of a Type 1 Error
As with any other error type inside an A/B test (or any different kind of research), Type 1 error also leads to incorrect conclusions.
From this, a series of new consequences arise, with the domino effect of one leading to another.
Incorrect conclusions lead to resources being wasted on unnecessary interventions. For example, if a fire alarm system is triggered due to a false positive, it could result in the evacuation of a building and a waste of emergency personnel resources.
In hypothesis testing, Type 1 errors undermine the credibility of a data analyst or the entity conducting the experiments.
Finally, Type 1 errors lead to missed opportunities. For example, if an experiment is falsely rejected in the experimentation phase, it could mean that brands miss out on potentially revenue-generating change on their website.
How to Avoid Type 1 Errors
So, how do you minimize a Type 1 error risk errors in your work?
First, before conducting the A/B test, set an appropriate level of significance (also known as alpha or confidence interval) that represents the maximum acceptable probability of making a Type 1 error.
The commonly used level of significance is 0.05, which means there is a 5% chance of making a Type 1 error.
You should also calculate the statistical power of the test.
The statistical power represents the probability of correctly rejecting the hypothesis when it is false.
Ensure that the test’s statistical power is adequate, typically above 80%.
If you are conducting multiple tests simultaneously, such as testing multiple landing page variations, control for the multiple testing. It will reduce the risk of having a Type 1 error occurring due to multiple comparisons.
Last but not least, you should always verify the results of your A/B test by conducting a follow-up test. For the second test, you could either use a different methodology or check the results against other relevant data.
This can help you ensure that any conclusions you draw from the test results are based on accurate data and not due to chance.
What Is a Type 2 Error?
On the other side of the coin, we have Type II (2) errors: occurring when the analyst fails to reject a null hypothesis that is actually false.
As with Type 1 errors, type II errors happen when the analyst concludes that there is no significant effect or relationship between variables. In reality, there is such an effect or relationship, so the conclusion is flawed.
Type II errors are also referred to as false negatives.
Here’s an example to illustrate the occurrence of a Type II error in other fields besides A/B testing for CRO.
Suppose someone suffers from a medical condition and undergoes a diagnostic test to confirm the condition.
The null hypothesis is that the person doesn’t have the condition, and the alternative hypothesis is that the person has the condition.
If the test fails to detect the condition, the person is falsely declared healthy, even though the condition is present.
This is a Type 2 Error.
Why Do Type 2 Errors Happen?
To avoid dealing with the consequences of a Type II error (which we’ll soon discuss), we must look at the situations favoring this error.
Just as bacteria forms when the environment isn’t sterile enough, certain factors lead to Type 2 errors messing up your experiments.
One of these factors is low statistical power.
Effective A/B testing is conditioned by a sample large enough to detect meaningful differences between the two variations tested.
If the sample size isn’t large enough, the test’s statistical power will be low, and it will fail to detect a real effect even if one exists.
Another factor concerns the test duration. You need to allow a sufficient period for your test to run if you want to detect meaningful differences between the two variations.
If you lack patience or need quick results, your test will be too short, thus failing to detect significant differences between the variations.
Another situation favoring Type 2 errors is when your variations are too similar to generate a difference.
Last but not least, Type 2 errors might appear when you conduct multiple tests simultaneously. In this case, the likelihood of a false negative result increases, as you can’t pinpoint the winning hypothesis.
Why Is It Important to Be Aware of Type 2 Errors?
As with Type 1 errors, false negatives also lead to missed opportunities and incorrect conclusions based on faulty data.
Failing to reject a hypothesis even though it’s false means implementing permanent changes on your website (or app) or even generating marketing procedures that won’t bring in the results you were hoping for.
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How to Avoid Type 2 Errors
Evidently, you don’t want to deal with false negatives in your tests and go on faulty avenues that only waste time and resources. So, how can you better prepare yourself to lower your error rates?
Firstly, you must determine the sample size before starting the test. Determine the required sample size based on the expected effect size and significance level.
This way, you’ll ensure the test has sufficient statistical power to detect meaningful differences between your variations.
Then, you need to allow the test to run sufficiently. Even if you need fast results and want to implement significant changes immediately, remember that a longer test duration will increase the chance of detecting a significant difference between the variations.
It would also be helpful to conduct a pre-test analysis.
This analysis highlights any potential sources of variability that may affect your test results. At the same time, it will empower you to take into account other issues that may affect your results, so you’ll be certain your results are valid.
Last but not least, you should carefully analyze your results and even conduct follow-up tests.
It will help you ensure that any conclusions you draw from the test results are based on accurate data, not due to chance, and reassure you about your data-driven decision being informed by reliable data.
Unfortunately, the tradeoff between Type 1 and Type 2 errors arises from the nature of statistical tests. To reduce the likelihood of one type of error, you often increase the likelihood of the other.
Consequences of a Type 2 Error
When Type 2 errors occur in multivariate testing, and people implement changes based on faulty test results, the brand has to deal with plenty of missed opportunities.
For example, suppose you wanted to test a new copy for your product description page. If your test is affected by a false negative, you will give up on the new copy, leading to missed opportunities to persuade more prospective customers to purchase the product.
At the same time, type 2 errors might embellish you into a false sense of security, encouraging you to implement changes that actually drive people away.
For example, let’s say you wanted to increase your prices. You test promoting more expensive products but on a small sample of your customers.
Some of them buy the products, leading you to increase the prices forever, without realizing you’re actively causing customers to churn because of the higher price points.
Finally, Type 2 errors also have ethical implications.
However, these implications are more prone in fields such as medicine or public health, with eCommerce being less affected.
Examples of a Type 2 Error
Let’s take a break from the world of statistics – the headache is real at this point – and look at some other examples of Type 2 errors happening in real life.
For example, imagine you’re working in the security department of an airport, and you just implemented a new security screening process.
Its purpose?
Identify passengers who may be carrying weapons or explosives.
In this case, a Type 2 error would occur if a passenger who is actually carrying a weapon or explosive is not flagged by the screening process and is allowed to board the plane.
Another example would be from the environmental field.
Suppose a company has been accused of polluting a river that runs through a nearby town. They deny the accusation and hire an independent testing agency to conduct an environmental impact study.
Surprise!
The study reveals no significant pollution levels in the river, so the company is cleared of any wrongdoing.
However, the study wasn’t sensitive enough, or researchers didn’t allow it to run enough so they could detect accurate pollution levels. In that case, a Type 2 error may have occurred, and innocent residents may continue to be exposed to harmful pollutants.
Wrap Up
Here is a quick recap before we reach our conclusions:
- Type 1 Errors (or false positives) occur when we reject a hypothesis when it is actually true.
- Type 2 Errors (or false negatives) occur when we fail to reject a hypothesis when it is actually false.
As they’re very similar, both types of errors have significant consequences.
Wasted resources, missing out on opportunities, and making harmful decisions are just a few of these consequences.
However, by being aware of Type 1 and 2 errors and taking steps to avoid them, you can conduct more reliable tests and draw more accurate conclusions.
Happy testing!
Frequently Asked Questions about Type 1 and 2 Errors
Type 1 error, also known as a “false positive,” occurs when a statistical test or hypothesis incorrectly rejects a true null hypothesis. In other words, it indicates that something significant was found when, in fact, there is no real effect or difference.
Type 2 error, also known as a “false negative,” happens when a statistical test or hypothesis fails to reject a false null hypothesis. It means that a real effect or difference exists, but the test fails to detect it.
Suppose you are running an A/B test on a website to determine whether changing the color of a CTA button from blue (Variant A) to green (Variant B) will increase the CTR. You randomly divide your website visitors into two groups: one sees the blue button, and the other sees the green button.
After running the test for a sufficient duration and collecting data, you analyze the results using statistical methods. However, your analysis concludes that there is no statistically significant difference between the two variants, meaning the test fails to detect an improvement in the CTR from using the green button.
If, in reality, the green button does indeed have a positive impact on the CTR, this would be an example of a Type 2 error. The test failed to identify the improvement, possibly due to factors such as a small sample size, high variability in user behavior, or insufficient statistical power.
Type 2 errors can occur due to various reasons, including: insufficient sample size or power, inadequate sensitivity of the test or high variability or noise in the data.
Type 1 error is often considered worse than Type 2 error due to its implications. For example, approving an ineffective drug or wrongly convicting an innocent person in a court trial.
Type 2 error, on the other hand, may result in missed opportunities or false negatives, but the consequences are generally less severe.
