Skip to main content

Where Experimentation goes wrong

The following contains a list of common pitfalls and mistakes that can happen when running A/B tests. It is important to be aware of these issues and to take steps to avoid them in order to ensure that your A/B tests are valid and reliable. It is by no means an exhaustive list.

Multiple Testing Problem

The multiple testing problem refers to the issue that arises when statistical hypothesis testing is performed on multiple variables simultaneously, leading to an increased likelihood of incorrectly rejecting a true null hypothesis (Type I error).

For example, if you test the same hypothesis at a 5% level of significance for 20 different metrics, the probability of finding at least one statistically significant result by chance alone is around 64%. This probability increases as the number of tests performed increases. This math assumes that the metrics are independent from one another, which in most cases for a digital application there will be some interaction between metrics (ie, page views is most likely related to sales funnel starts, or member registration to purchase events)

To address this problem, various multiple comparison correction methods can be used, such as the Bonferroni correction, False Discovery Rate (FDR) correction, or the Benjamini-Hochberg procedure. These methods adjust the significance level or the p-value threshold to account for the increased risk of false positives when multiple comparisons are made.

It's essential to be aware of this issue and select an appropriate correction method when conducting multiple statistical tests to avoid false discoveries and improve the accuracy and reliability of research findings. If you are using a high number of metrics, draw conclusions from the test thoughtfully and if you may consider running a follow up test just to test that one result or metric.

Texas Sharpshooter Fallacy

The Texas sharpshooter problem is a cognitive bias that involves cherry-picking data clusters to suit a particular argument, hypothesis, or bias. The name comes from the idea of a Texan marksman shooting at a barn and then painting a target around the cluster of bullet holes to create the appearance of accuracy. As the story goes, he then showed his neighbors and convinced them he was a great shot. It is closely related to the Multiple Testing Problem/Multiple Comparison Problem.

In the context of data analysis and statistics, the Texas sharpshooter problem refers to the danger of finding apparent patterns or correlations in data purely by chance and then using those patterns as if they were meaningful. This can lead to false conclusions and misguided decision-making. Texas sharpshooter problem is relevant in the sense that if you analyze the results of a test without a clear hypothesis or before setting up the experiment, you may be susceptible to finding patterns that are purely due to random variation. If you analyze the data in multiple ways or look at various subgroups without adjusting for multiple comparisons, you might identify spurious patterns that do not actually represent a true effect.


P-hacking, or data dredging, is a statistical fallacy that involves manipulating or analyzing data in various ways until a statistically significant result is achieved. It occurs when researchers or analysts repeatedly test their data using different methodologies or subsets of the data until they find a statistically significant result, even if the observed effect is due to chance.

In the context of A/B testing, p-hacking can be a significant concern. A/B testing involves comparing at least two versions (A and B) to determine which performs better. The danger of p-hacking arises when analysts, either consciously or unconsciously, explore different metrics, time periods, or subgroups until they find a statistically significant difference between the A and B groups.


The peeking problem refers to the issue of experimenters making decisions about the results of an experiment based on early data. The more often the experiment is looked at, or ‘peeked’, the higher the false positive rates will be, meaning that the results are more likely to be significant by chance alone. Peeking typically applies to Frequentist statistics, which are statistically valid at their predetermined sample size. However, Bayesian statistics can also suffer from peeking depending on how decisions are made on the basis of Bayesian results.

The peeking problem in A/B testing occurs when the experimenter looks at the data during the experiment and decides to stop the test early based on the observed results, rather than waiting until the predetermined sample size or duration has been reached. This can lead to inflated false positive rates, as the results are more likely to be significant by chance alone if the experimenter stops the test early based on what they see in the data. The more often the experiment is looked at, or ‘peeked’, the higher the false positive rates will be.

To avoid the peeking problem in A/B testing, it's important to use a predetermined sample size or duration for the experiment and stick to the plan without making any changes based on the observed results. This helps to ensure that the statistical test is valid and that the results are not influenced by experimenter bias.

Another way to avoid the peeking problem in A/B testing is to use a statistical engine that is less susceptible to peeking, like a Bayesian with custom priors, or to use a method that accounts for peeking like Sequential testing.

Problems with client side A/B testing

Client-side A/B testing is a technique where variations of a web page or application are served to users via JavaScript on the user's device, without requiring any server-side code changes. This technique can offer a fast and flexible way to test different variations of a website or application, but it can also present some potential problems, one of which is known as "flickering."

Flickering is a problem that can occur when the A/B test is implemented in a way that causes the user interface to render the original version, then flash or flicker as the variations are loaded. This can happen when the A/B test code is slow to load or when the A/B testing library is lacking performance. As a result, the user may see the original version of the page briefly before it is replaced with one of the variations being tested, leading to a jarring and confusing user experience. This flickering can lead to inaccurate or unreliable test results. Rather counterintuitively, flickering may have a positive effect on the results, sometimes the flashing may draw a users attention to that variation, and cause an inflation in the effect.

To avoid flickering in client-side A/B testing, it is important to implement the test code in a way that minimizes the delay between the original page and the variations being tested. This may involve preloading the test code or optimizing the code for faster loading times. GrowthBook’s SDKs are built for very high performance, and allow you to use client side A/B testing code inline, so there are no blocking 3rd party calls.

You can also use an alternative technique such as server-side testing or redirect-based testing to avoid flickering issues. If loading the SDK in the head does not sufficiently prevent flicking, you can also use an anti-flickering script. These scripts hide the page while the content is loading, and reveal the page after the experiment loaded. The problem with this is that while it technically prevents flickering, it slows how quickly your site appears to load.

Redirect tests (Split testing)

Redirect-based A/B testing is a technique where users are redirected to different URLs or pages based on the A/B test variation they are assigned to. While this technique can be effective in certain scenarios, it can also present several potential problems that should be considered before implementation.

SEO: Redirects can negatively impact SEO, as search engines may not be able to crawl the redirected pages or may see them as duplicate content. This can result in lower search engine rankings and decreased traffic to the site.

Load times/User experience: Redirects can increase page load times, as the browser has to make an additional HTTP request to load the redirect page. This can result in slower load times, which can impact user experience, conversion rates, and A/B test outcomes.

Data accuracy: Redirects can also impact the accuracy of the test results, as users may drop off or exit the site before completing the desired action due to a slower load time or confusing user experience. It can also be harder technically to fire the tracking event, causing a loss in data.

To mitigate these problems, it's important to carefully consider whether redirect-based A/B testing is the most appropriate technique for your specific use case. If you do choose to use redirects, it's important to implement them correctly and thoroughly test them to ensure that they do not negatively impact user experience or test results. Additionally, it may be helpful to use other techniques such as server-side testing or client-side testing to supplement redirect-based testing and ensure the accuracy and reliability of the test results like testing on the edge or using middleware to serve different pages.

Semmelweis Effect

The Semmelweis effect refers to the tendency of people to reject new evidence or information that challenges their established beliefs or practices. It is named after Ignaz Semmelweis, a Hungarian physician who, in the 19th century, discovered that hand washing could prevent the spread of infectious diseases in hospitals. Despite his findings, he was ridiculed and ignored by his colleagues, and it took many years for his ideas to be accepted and implemented.

In the context of A/B testing, the Semmelweis effect can manifest in several ways. For example, a company may have a long-standing belief that a certain design or feature is effective and produces good results, and may not want to experiment with it because everyone knows it ‘correct’. Even if an experiment is run against this entrenched belief, and the results of an A/B test challenge established norms, there may be resistance to accept the new evidence and change the established practice.

To avoid the Semmelweis effect in A/B testing, it is important to approach experimentation with an open mind and a willingness to challenge established beliefs and practices. It is crucial to let the data guide decision-making and to be open to trying new things, even if they go against conventional wisdom or past practices. It is also important to regularly review and evaluate the results of A/B tests to ensure that the company's beliefs and practices are aligned with the latest evidence and insights, and haven’t changed over time.

Confirmation Bias

Confirmation bias refers to the tendency to favor information that confirms our preexisting beliefs and to ignore or discount information that contradicts our beliefs. In the context of A/B testing, confirmation bias can lead to flawed decision-making and missed opportunities for optimization.

For example, if a company believes that a certain website design or feature is effective, they may only run A/B tests that confirm their beliefs and ignore tests that challenge their beliefs. This can lead to a bias towards interpreting data in a way that supports preexisting beliefs, rather than objectively evaluating the results of the tests. Or a PM may believe a new version of their product will be superior, and only acknowledge evidence that confirms this belief.

Confirmation bias can also manifest in the way tests are designed and implemented. If a company designs an A/B test in a way that biases the results towards a particular outcome, such as by using a biased sample or by selecting a suboptimal metric to measure success, it can lead to misleading results that confirm preexisting beliefs.

To avoid confirmation bias in A/B testing, it is important to approach experimentation with an open and objective mindset. This involves being willing to challenge preexisting beliefs (Semmelweis) and being open to the possibility that the data may contradict those beliefs. It also involves designing tests in a way that is unbiased and that measures the most relevant and meaningful metrics to evaluate success. Having multiple stakeholders review and evaluate the results of A/B tests can help ensure that decisions are based on objective data, rather than personal biases or beliefs.


HiPPO is an acronym for the "highest paid person's opinion." In less data-driven companies, decisions about what product to build or which products to ship are made by HiPPOs. The problem with HiPPOs is that it turns out their opinions are no more likely to be right than anyone else's opinions, and are therefore often wrong. But due to their status they may resist against experimentation to preserve their status or ego. The HiPPO effect is a common problem in many organizations, and it can lead to poor decision-making and missed opportunities for your product.


When experiment results challenge existing norms or an individual’s beliefs, it can be easy to blame the data. For this reason, having a trustworthy A/B testing platform is extremely important. There must be ways to audit the results, and look into if there was any systemic or specific problem affecting the results of the experiment. Running A/A tests can help build trust that the platform is working correctly. Trust in an experimentation platform is built over time, and care must be taken to not just dismiss results that are counterintuitive.

Twyman's Law

Twyman's law is a principle in statistics that states that any data that is measured and collected will contain some degree of error, and that this error is an inherent part of the data. It is named after the British statistician Maurice G. Kendall Twyman.

In the context of A/B testing, Twyman's law suggests that there will always be some level of variability or uncertainty in the results of an A/B test due to factors such as random chance, sample size, or measurement error. It is often phrased as:

Any data or figure that looks interesting or different is usually wrong

If you notice a particularly large or unusual change in the results of an experiment, it is more likely to be the result of a problem with the data or an implementation than an actual result. Before you share the results, make sure that the effects are not the result of an error.

Goodhart's Law

Goodhart's law is a concept in economics that states that when a measure becomes a target, it ceases to be a good measure. In other words, once a metric becomes the sole focus of attention and effort, it loses its value as an indicator of the desired outcome.

When it comes to A/B testing, Goodhart's law can apply in several ways. For example, if a specific metric such as click-through rate or conversion rate becomes the sole focus of an A/B test, it can lead to unintended consequences such as artificially inflating the metric while neglecting other important aspects of the user experience. This can happen because individuals or teams may optimize for the metric being measured rather than focusing on the broader goals of the A/B test, such as improving user engagement or increasing revenue.

To avoid the negative effects of Goodhart's law in A/B testing, it is important to choose the right metrics to track and analyze, and to use a variety of metrics to evaluate the effectiveness of the test. It is also important to keep in mind the broader goals of the test and to avoid tunnel vision on any one metric. Goodhart's law is more likely to happen when you are using proxy metrics, instead of the real KPIs you’re trying to improve - an example of this might be items added to a cart being used as a proxy for purchases. Also If the proxy metric is not strongly causally linked to the target metric, pressing hard on the proxy may have no effect on the goal metric, or might actually cause the correlation to break.

Simpson's Paradox

Simpson's paradox is a statistical phenomenon where a trend or pattern appears in different groups of data but disappears or reverses when the groups are combined. In other words, the overall result may be opposite to what the individual subgroups suggest.

This paradox can arise when a confounding variable (a variable that affects both the independent and dependent variables) is not taken into account while analyzing the data.

Simpson's paradox was famously observed at the University of California, Berkeley in 1973, where it had implications for gender discrimination in graduate school admissions.

At the time, it was observed that although the overall admission rate for graduate school was higher for men than for women (44% vs. 35%), when the admission rates were broken down by department, the reverse was true for many of the departments, with women having a higher admission rate than men in each department. In the Department of Education, for example, women had a 77% admission rate compared to men's 62% admission rate.

The paradox was resolved by examining the application data more closely and considering the impact of an important confounding variable, which was the choice of department. It was discovered that women were more likely to apply to departments that were more competitive and had lower admission rates, while men were more likely to apply to less competitive departments with higher admission rates.

When the data was reanalyzed, taking into account the departmental differences in admission rates, it was found that women actually had a slightly higher overall admission rate than men, suggesting that there was no discrimination against women in the admissions process. This case study illustrates how Simpson's paradox can occur due to the influence of confounding variables, and how it can lead to misleading conclusions if not properly accounted for in the analysis. To avoid the Simpson's paradox in experimentation, it is essential to analyze the data by considering all relevant variables and subgroups. It is crucial to ensure that the experimental groups are similar in terms of demographics and behavior, and to use statistical techniques that account for confounding variables.

Ethical considerations

Experimentation judges the outcome of changes by looking at the impact it has on some set of metrics. But the seeming objectivity of the results can hide problems. The simplest way this can go wrong is if your metrics are tracking the wrong things, in which case you’ll have garbage in and garbage out. But it is also possible for the metrics to not capture harm that is being done to some subsets of your population.

Experimentation results work on averages, and this can hide a lot of systemic biases that may exist. There can be a tendency for algorithmic systems to “learn” or otherwise encode real-world biases in their operation, and then further amplify/reinforce those biases.

Product design has the potential to differentially benefit some groups of users more than others; It is possible to measure this effect and ensure that results account for these groups. Sparse or poor data quality that leads to objective-setting errors and system designs that lead to suboptimal outcomes for many groups of end users. One company that does this very well is the team at LinkedIn, you can read about their approach here.