In digital marketing, A/B testing involves comparing two versions of a webpage to determine which one delivers a higher conversion rate. This conversion could mean making a purchase, signing up for a newsletter, completing a form, or whichever is the desired action for that specific experiment.
Its purpose is to systematically improve website performance by identifying elements that influence visitor behavior.
However, before designing your A/B test, you must first establish both the null hypothesis and the alternative hypothesis.
The null hypothesis states that sample observations appear solely due to random chance.
In the context of A/B testing, the null hypothesis suggests that no difference exists between the control and variant groups.
In terms of A/B testing, the alternative hypothesis suggests that there is a difference between the control and variant groups.
For example, during an experiment, your null hypothesis might state that changing the color of the website’s CTA button from green to blue will not lead to any notable difference in conversion rates between the control and variant groups.
On the other hand, the alternative hypothesis states that the same action will deliver a noticeable difference in conversion rates.
Why use an alternative hypothesis?
While the null hypothesis says there’s no difference, the alternative hypothesis keeps you open to the possibility that changes could have a meaningful impact on metrics like user behavior and conversion rates.
Paying attention to the alternative hypothesis involves actively looking for ways to improve your website and overall marketing strategies.
It encourages you to stay curious and explore your data for different ideas to make our websites better – it’s a way to overcome organizational inertia and change the status quo to better meet the needs of your visitors.
Understanding the Alternative Hypothesis
To recap: the alternative hypothesis presents a contrasting idea to the null hypothesis.
While the null hypothesis suggests a certain outcome is true, the alternative hypothesis proposes that the opposite is true.
It’s often the hypothesis you investigate when aiming to challenge the null hypothesis.
If you collect sufficient evidence to support the alternative hypothesis, it replaces the null hypothesis as the explanation for the observed results.
Different types of alternative hypotheses can play a role in your CRO experiments, including:
One-tailed directional
In a one-tailed directional test for CRO, the alternative hypothesis focuses on changes happening in one direction only.
For instance, it might evaluate whether a particular change leads to an increase or decrease in conversion rates, but not both at the same time.
If the CRO specialist suspects the change will lead to a decrease in bounce rates, they might describe the test as left-tailed.
Conversely, if they predict the change will increase the click-through rates, they might describe the test as right-tailed.
Two-tailed or nondirectional
In a two-tailed or nondirectional test for CRO, the alternative hypothesis suggests that changes are happening, but it doesn’t specify whether those changes are positive or negative.
This means the test acknowledges that variations exist, but it doesn’t assume a particular direction of change.
In the context of CRO, a two-tailed test might suggest that changes to a website’s layout affect user behavior, without indicating whether the changes will increase or decrease conversion rates.
Formulating an Effective Alternative Hypothesis
To run the best experiments, make sure you base your hypotheses on psychology, data from web analytics, customer research, and what you’ve found out from previous experiments.
A good hypothesis follows a simple structure:
“If [we do this], then [this will happen].”
Here are a few examples to help you understand:
- If we put a customer testimonial on the product page, then more people will feel confident and buy.
- If we mention our fast delivery in the product description, then more people will notice it and fewer people will leave the website without doing anything.
- If we change the text on the button to ‘Add to cart’, then people will feel less worried and add more things to their cart.
Besides the basic structure, you must also pay attention to the contents of your hypothesis. Ensure your hypothesis is:
- Relevant: the hypothesis must align with the KPIs you’re using to evaluate the experiment.
- Unambiguous: be very clear in specifying which change will be tested.
- Measurable: ensure your results can be measured with the available tools in your suite.
- Specific: include all crucial information about the variables tested in the hypothesis.
The Role of the Alternative Hypothesis in A/B Testing
Finding meaningful patterns in your data can be challenging, especially when your data is susceptible to random fluctuations.
Random noise can create apparent patterns purely by chance, adding unneeded complexity to your interpretation process.
One widely used method to avoid random occurrences directing your strategies is through hypothesis testing.
This approach is much more structured, allowing you to evaluate the likelihood of observed patterns that have appeared merely by random chance.
Now, let’s see how the alternative hypothesis shapes the very core of your experimentation process.
Guiding Experimental Design
At the heart of every A/B test lies a hypothesis waiting to be explored.
The alternative hypothesis provides the framework for our experiments, guiding you in crafting specific changes and variations to test against the control group.
It’s the “the voice of reason” that ensures your tests remain purposeful and focused, driving you towards meaningful discoveries.
Determining the Metrics for Measurement
Remember when we discussed how your hypothesis must be measurable in order to deliver effective insights?
Well, metrics are the pulse of A/B testing; it’s through them that you’re evaluating the efficiency of your experiments.
With that in mind, the alternative hypothesis outlines expected differences between the control and variant groups, revealing the metrics you need to track to evaluate these differences.
These metrics then become our benchmarks, helping you validate your hypotheses.
Evaluating Test Outcomes
At its end, A/B testing is all about discovering alternative ways to design your website – both from a UI and a UX perspective.
The alternative hypothesis guides your interpretation of test results and informs decision-making processes.
When the data aligns with the alternative hypothesis, it signals a breakthrough – your proposed changes have made a tangible impact.
This validation empowers you to move forward with confidence, armed with insights derived from experimentation.
Conversely, if the data disproves your hypothesis, it forces you to recalibrate your approach and explore new variations in your experiments.
Setting Up A/B Tests with the Alternative Hypothesis in Mind
So – how do you test your alternative hypothesis during an A/B test?
The process is pretty straightforward:
- you identify the variable you want to test,
- choose the metrics that will evaluate your hypothesis,
- and finally, ensure your sample size delivers statistically significant results.
Now, let’s dive into the process and discuss each step in detail.
Define the Goals
Begin by aligning the objectives of your A/B testing with the core business goals of your company.
For example, imagine you’re a product manager at “Company X” in its startup phase. The company aims to boost user numbers, particularly active users measured by the Daily Active Users (DAU) metric.
Assuming this can be achieved by improving retention rates or increasing new registrations, you’ll focus your efforts accordingly.
Define the Metric
Determine the metric that will serve as the yardstick for evaluating the success of your A/B test.
While conversion rates are commonly used, you might opt for an intermediate metric like the Click-Through Rate (CTR).
In our example, the registration rate—calculated by the number of new users who register divided by total new site visitors—serves as the metric.
KPI tracking in Omniconvert Explore.
Develop a Hypothesis
Craft a hypothesis outlining the expected changes and the rationale behind them.
For instance, suppose you want to test whether changing the main page image increases registration rates.
The hypothesis could state: “If the main registration page image is changed, more new users will register due to the image better conveying product values.”
Identify both the null and alternative hypotheses to guide your testing approach.
Prepare an Experiment
To make sure your experiment is set up for success, follow these steps:
- Create a new variation (B) reflecting the changes to be tested.
- Define control and experimental groups, specifying user selection criteria and group size.
- Randomly assign users to version A or B to ensure unbiased results.
- Determine the statistical significance level (α) and minimum sample size required for each version.
- Establish a time frame for the experiment based on daily traffic and sample size.
Experiment setup in Omniconvert’s complete A/B testing tool – Explore.
Conduct the Experiment
To execute the experiment diligently, it is crucial to coordinate with team members for smooth execution.
The next step is requesting access to a closed test site, if available, for data verification purposes.
Finally, you have to validate the experiment setup and avoid premature result analysis – this way your experiment will be both accurate and reliable.
Analyze the Results
To analyze the results, delve into the data to assess the performance of each version and determine statistical significance. Start by calculating success metric values for versions A and B.
Segment the data by relevant parameters, such as platforms or geographical regions, to gain deeper insights.
Evaluate statistical significance using p-values and significance levels to ascertain the validity of your findings. Interpret the results: if version B outperforms version A, your hypothesis is validated.
It’s important to note that A/B testing outcomes can vary; version B may win, lose, or show no significant difference. Understanding these results informs future testing and optimization efforts, driving continuous improvement in achieving business objectives.
Need more? Here’s how you can interpret experiment results using Omniconvert Explore. Check out the entire process and see for yourself how intuitive and straightforward it is!
Statistical Significance and the Alternative Hypothesis
In order to either accept or reject your hypothesis, your experiments must reach a certain significance level – which becomes the criteria you’re using to assess the results.
The significance level, often referred to as the alpha level or critical value, is commonly denoted by the Greek letter alpha (α) and represents a probability value.
Moreover, to determine the strength of evidence supporting either hypothesis (null or alternative), you’ll measure it against the p-value.
The p-value quantifies how likely it is to observe the data or something more extreme if the null hypothesis were true. Essentially, it indicates the probability of obtaining the observed results or more extreme results when the null hypothesis is true. A lower p-value suggests stronger evidence against the null hypothesis.
When conducting hypothesis testing, researchers compare the calculated p-value to a pre-determined significance level, often denoted as α.
The significance level represents the threshold for determining statistical significance. Commonly used significance levels include 0.05, 0.01, or 0.1.
If the calculated p-value is lower than the significance level (e.g., p < 0.05), it indicates that the observed difference is statistically significant. In this case, the evidence favors the alternative hypothesis over the null hypothesis.
Thus, researchers reject the null hypothesis in favor of the alternative hypothesis.
>> click here to read more about Statistical Significance in A/B testing.
Type I and Type II Errors
Balancing risks in hypothesis testing involves understanding Type I and Type II errors.
A Type I error occurs when the null hypothesis is incorrectly rejected, indicating significance when there is none. This is basically a false positive.
Conversely, a Type II error appears when the null hypothesis is erroneously accepted, failing to detect a true effect or difference. This is essentially a false negative.
Lowering the significance level decreases the risk of Type I errors but increases the likelihood of Type II errors, and vice versa.
To balance these risks, carefully consider the consequences and the context of the hypothesis test.
>> here’s a full article on Type I and Type II errors.
Power Analysis in the Context of the Alternative Hypothesis
Before undertaking power analysis, we must first understand the idea of “a statistical test power.”
The statistical power of a test refers to its likelihood of achieving statistical significance, thus delivering meaningful conclusions.
Unfortunately, power analysis is frequently overlooked in CRO.
There are various forms of power analyses, each serving distinct purposes.
Typically, power analysis is conducted during the initial design phase of a test to determine the required sample size.
Alternatively, post hoc analysis, another type of power analysis, occurs after the study concludes.
Post hoc analysis addresses questions such as:
- determining the necessary sample size to detect a particular effect size
- identifying the minimum effect size detectable with the given sample size,
- and evaluating the power of the test procedure.
These questions, among others, can be addressed through power analysis.
Should you ever need to increase the power of an A/B test, try these ideas:
- increase sample size
- reduce variability within groups
- select appropriate statistical methods
- minimize experimental noise
- focus on relevant variables
Interpreting Results in Light of the Alternative Hypothesis
As you review your A/B test outcomes, begin by conducting statistical tests to determine if your A/B test attained a sufficiently large sample size.
Fortunately, most A/B testing tools automate this process, allowing you to review results on your dashboard effortlessly.
Subsequently, identify the variant that outperformed others in terms of your primary metric. This early identification provides a frontrunner for the version you may eventually roll out to all your site visitors.
A good practice is examining your test results across multiple dimensions of customer behavior beyond just conversion rates, which typically serve as the primary metric for A/B testing.
Consider metrics like click-through rate, time spent on page, and revenue generated to gain a comprehensive understanding of your web pages and marketing campaigns.
For example, you might find that a targeted approach resulted in fewer conversions than expected- however, people who converted are higher-valued customers, with increased AOVs and more chances to return for future purchases.
Finally, besides analyzing the A/B test data, it’s crucial to consider external variables that might influence your results.
For instance, conducting an A/B test during peak holiday seasons or after significant industry events might skew your results. While controlling all variables is impractical, strive to ensure that your test generates generalizable results.
If you suspect external factors significantly impacted your A/B test results, consider repeating the test to validate your findings.
As for accepting or rejecting the alternative hypothesis, the rule is pretty straightforward:
When the p-value falls below the alpha threshold, the null hypothesis is rejected, and the alternative hypothesis is accepted.
Conversely, if the p-value exceeds the alpha threshold, we do not reject the null hypothesis.
Making Decisions Based on the Outcome of the Hypothesis Test
By the end of this process, you’ll have a clear idea of whether the new variations you tested in your A/B test have achieved the desired results.
If you find that there isn’t a significant difference between the variations you tested, that’s perfectly okay because testing is all about exploring different hypotheses. In such cases, you might decide to stick with your original design or continue experimenting to find improvements.
On the flip side, if you discover a new variation that outperforms your previous efforts, it’s time to celebrate and consider rolling it out to your entire audience.
There are various possible outcomes at this stage, and they can’t all be covered here. For example, if you have a variant with a high conversion rate but mediocre revenue generation, deciding whether to keep testing can be challenging.
The key advice is to view A/B testing as part of an ongoing process of optimizing conversion rates and continuously refining your website.
A Practical Case Study
Here’s how the A/B test with the alternative hypothesis looks in action, based on a real experiment we conducted for AliveCor.
AliveCor is a leading brand in the digital health technology sector, specializing in innovative solutions for heart health monitoring.
Their portable electrocardiogram (ECG) devices, integrated with user-friendly mobile applications, empower individuals to monitor their cardiovascular well-being proactively.
AliveCor faced the challenge of promoting its newly launched single lead device, KardiaMobile Card, without negatively impacting the sales of its existing devices.
KardiaMobile Card aimed to serve as an upgraded alternative to the widely acclaimed KardiaMobile, which had garnered attention across the healthcare industry.
Previous experiments revealed that users tended to interact more frequently with highlighted elements and products on AliveCor’s website.
Based on this insight, we hypothesized that adding a “New” badge to the KardiaMobile Card product detail page and the product tile on the listing page would increase the conversion rate across all devices.
Following the implementation of the “New” badge:
- Conversion Rate increased by 25.17%
- Revenue per user increased by 29.58%
- Confidence level of success was 99.4%
Lessons Learned
The experiment demonstrated that a simple highlighting badge on new products significantly boosted the conversion rate without diminishing the sales of other products.
This finding underscores the effectiveness of strategic product highlighting in driving user engagement and sales.
Moving forward, AliveCor can leverage these findings to inform its marketing strategies and product promotions.
By continuing to emphasize new product offerings through strategic highlighting and promotion, AliveCor can effectively engage users and drive sales while maintaining a positive user experience.
This experiment highlights the importance of data-driven decision-making and iterative optimization in achieving business objectives in the digital health technology sector.
>> check out the full Case Study here.
Challenges and Considerations
As powerful a practice as it can be, the A/B test isn’t without its pitfalls. Here are some common challenges to be aware of:
1. Vague Hypotheses
Formulating a clear and specific alternative hypothesis is crucial for meaningful A/B testing.
Vague hypotheses can lead to ambiguous results and misinterpretation of data.
Without a precise hypothesis, it becomes challenging to draw actionable insights from the test outcomes.
Remember that your hypothesis must be relevant, unambiguous, measurable, and specific.
2. Overfitting
Overfitting occurs when a hypothesis is tailored too closely to the observed data, leading to inflated results that may not generalize well to other scenarios.
In A/B testing, overfitting can occur when the hypothesis is overly complex or when the test is run on a small sample size.
It’s important to strike a balance between complexity and generalizability to avoid overfitting.
3. Ignoring External Factors
As you saw earlier, external factors can, and will, influence your test results; failing to consider them can lead to biased conclusions.
Variables like seasonality, marketing campaigns, and user behavior trends can all impact the outcome of an A/B test.
Make sure you take them into consideration, so you don’t end up with inaccurate interpretations of the data and ineffective optimization efforts.
Ethical Considerations in A/B Testing
Finally, there are a few additional considerations, this time on the ethics’ side. These include:
Informed Consent
Users should be informed about their participation in A/B tests and any potential changes to their experience.
Transparency about the testing process and its implications is essential for maintaining trust.
Providing clear and accessible information about the purpose of the test, how data will be used, and any potential impact on the user experience helps ensure that users can make informed decisions about their participation.
Fair Treatment
It’s essential you ensure that all users are treated fairly throughout the A/B testing process.
This means that variations tested should not disproportionately impact certain groups negatively.
Careful consideration should be given to factors such as demographics, behavior patterns, and accessibility requirements to avoid inadvertently discriminating against specific segments of the user population.
Data Privacy
Respecting user privacy is paramount in A/B testing.
This involves anonymizing data and adhering to relevant data protection regulations to safeguard user information.
Avoid collecting unnecessary personal information during testing and ensure that data is handled securely and responsibly.
Prioritizing data privacy will build trust with your users and mitigate the risk of privacy breaches.
Embracing Continuous Learning
It’s important to note that A/B testing isn’t just a one-time activity; it’s an ongoing journey of learning and optimization.
Through continuous testing, you will refine your digital experiences over time, leading to improved user engagement and conversion rates.
Moreover, digital landscapes are dynamic, with user preferences, technology, and market trends constantly evolving.
Continuous testing ensures that websites and applications remain relevant and effective in meeting user needs.
By staying proactive and responsive to changes, you can adapt their digital strategies to align with shifting consumer behaviors and industry trends.
Lastly, when you embrace a culture of continuous testing and optimization, you gain a significant advantage.
You’re continually refining your digital offerings, thus staying ahead of the curve and responding to market changes better.
Continuous testing enables you to identify emerging opportunities, address user pain points, and maintain your position as an industry leader.
Wrap Up
Whether you’re launching a new landing page or fine-tuning the subject line of an email marketing campaign, A/B testing stands as a vital tool for creating user experiences that drive conversions and boost sales.
Testing is the backbone of making informed decisions based on solid data.
However, it’s important to note that the efficacy of your A/B testing depends on the thoroughness of your results analysis.
You must understand the significance level of your tests, examine various KPIs, and formulate a hypothesis that is relevant, unambiguous, measurable, and specific.
While it sounds like a lot of work, it’s also necessary work.
Once you’re on top of the nuances of this critical stage of A/B testing, you’ll unlock the potential to achieve results beyond your expectations.
