This is a great question, one that's becoming especially relevant as more companies (like Quora [1]) take a data-driven approach to product development. At it's core, this isn't a question about "Designers vs Engineers" (though they may be the stereotypical vanguards of data-driven and intuition-driven decision making), but rather a more general one: How do you make product decisions using both empirical data (statistics, usage, test results) and qualitative data (prior experience, intuition, beliefs about human nature)?

It helps to think about this tension in two buckets: one where data confirms intuition, and one where it doesn't. To make this concrete, imagine a new feature being designed for a product, which is first A/B tested to a subset of users. Team members make predictions about the outcome of the test, based on their knowledge and experience, then compare those intuitions to the data. Simplifying slightly, here are four potential scenarios:

In half the scenarios (I and IV), intuition and data agree: The people
working on the feature predict that there will be positive or negative impact, and the data supports that prediction. [2] There are a number of meaningful benefits to these scenarios that are worth calling out explicitly, as they set the stage for the more difficult scenarios of disagreement:

Intuition is built from data.
The fabric of qualitative predictions, intuition and gut instinct, is woven from threads of concrete data findings. A theory of human behavior (e.g. "People respond to short, simple language") might one day be part of your knowledge and experience. But forming that intuition is best done through a series of rigorous, focused tests. Even confirming existing theories through data helps build confidence in what you think you know, exposes nuance, and confirms that they hold true across multiple contexts.

Quantifying impact helps make tradeoffs.
Even the best qualitative theory rarely includes predictions about degree of impact. Testing and confirming improvements results in an outcome that can be weighed against other potential changes, and balanced against the work necessary to build and support that change.

Confirming impact keeps you sane.
The question asks this specifically, and it's true that having your intuition challenged by data is confidence-shaking, so it's worth calling out the converse here: Having data confirm your prediction feels good, gives you faith in your gut, and can help power you through the WTF!? moments that future tests will surely deliver.

When data confirms intuition, it's an easier outcome to feel good about and move forward with. Harder to reconcile is when the data disagrees with intuition, i.e. Scenarios II and III. Underneath the inherent frustration and bewilderment that comes with these outcomes, there can be tremendous learning opportunities:

Unexpectedly bad data forces a reconsideration of assumptions.
When I encounter an Intuition False Positive, my first instinct is always to reexamine every assumption baked into a design. Why exactly did I believe in this change? What theory underlies that prediction? What are all the steps I'm expecting will happen, and are they actually happening? Are people not starting a flow? Not finishing it? Using it perfectly, but cannibalizing usage from another part of the site? Design is surely "more than the sum of its parts", but if a design isn't working, it's often one of the parts that is broken.

Unexpectedly good data exposes new opportunities for impact.
When I find myself in an Intuition False Negative, it's usually because I've overlooked a potentially meaningful area of impact. I may not believe that a language change, or button color, or new UI adornment will have any impact, but when it does, it forces me to consider what else I've overlooked. What are the biggest dead ends in a flow? What seems simple to me but is actually confusing to new users? What information might people need in this scenario that they don't have? In short, what are all the possible pressure points that can be moved to achieve the goals of a product. Really thoughtful considering of these results, without biases or assumptions, creates fertile ground for brainstorming new ideas with potentially high impact.

Every time a product change produces unexpected data, I learn an enormous amount about the specific feature that's been built, how people use a product, and future opportunities for impact. Ultimately these learnings create more future occurrences of Confirmed Intuition. It can be a painful road, as any challenging learning opportunity is, but the best lessons often come from these moments [3].

To really reap the benefits of this data-driven approach to product development, (at least) two things are critical:

Test Falsifiable, Named Hypotheses
To learn from data and strengthen intuition, it's critical to be able to draw lessons from results: if data says that "The test group performed 5% more key actions than the control group", what abstract learning can be drawn from this? Having this concise takeaway is critical for using these results in the future. Test groups should be conceived to test as few variables as possible, and those variables should have clear names: "I think people will sign up for a service that seems affordable, so I'm going to put our price information on the homepage." If signups increase, then the lesson should be that pricing information increases conversion [4].

If the test is "I'm going to try a bunch of different line-heights to see which one converts best," and one of them does, you're not left with a useful lesson: Is "line-height: 1.85em converts best" really a finding you can extrapolate to other scenarios? What is the hypothesis for why this is true? Is it even true at all? (If you're looking for a 95% confidence interval, 1 out of 20 tests will show good results without there being a real difference). Without thoughtful test design, your confounding results won't strengthen your intuition moving forward, and instead will paralyze you by wanting to reap the benefits of a test that you don't really understand.

Accept that Testing and Product Development are Non-Linear
A concern with frequent A/B testing of small iterations is that implementing each positive change will lead to a piecemeal, incoherent design, that doesn't respect (in the words of the question) that "design is more than the sum of its parts." This is surely a concern, but only if test results are considered dogma to be used blindly, without consideration of context. Much better is to see data as information and guidelines to be incorporated into future, thoughtful iterations. Specific test results can inform theories and conversations about behavior, show the high water mark of impact for a potential change, and provide a framework for making tradeoffs between possible product directions. This protects the informative power of data, while maintaining consistent, thoughtful, holistically considered experiences for users.

Data-driven product development, even practiced perfectly, is an emotional roller coaster: The confidence-inspiring highs of clear impact is matched only by a bewildering negative impact that questions everything you thought you knew about the world. But if tests are well-designed explorations of a theory, results are used as moments of learning and improvement, and the eventual feature released is informed and not dictated by these findings, the result is both a product and product designer who is better for the process.

[1] Quora is a deeply data-driven company: We have a team of data scientists who deliver nuanced analyses of tests and usage patterns, a group of engineers and designers who believe in using that data thoughtfully, and a company culture of relying on data rather than ego, politics, or emotional investment. Also, we're hiring: http://www.quora.com/about/jobs

[2] In practice, Scenario I is far more likely than Scenario II: If no one predicts the positive impact from a change, it probably won't get built.

[3] For a funny and honest look at the experience, see Denial, anger, bargaining, depression, and acceptance: The A/B testing lifecycle.

[4] With the obvious caveat that you'd want further and different tests to confirm the source of these results.

Thanks to David Cole being a sounding board and editor for these scattered thoughts.

The star schema is one of the two different ways of organizing data warehouses, based dimension tables that describe the information contained within a fact table: Data Warehouse Concepts: Traditional vs. Cloud - Star Schema vs. Snowflake Schema (the another way is snowflake schema - it splits the fact table into a series of normalized dimension tables).

The star schema takes the information from the fact table and splits it into denormalized dimension tables. The emphasis for the star schema is on query speed. Only one join is needed to link fact tables to each dimension, so querying each table is easy.

Yes, A/B testing is one of the most effective ways to analyze design effectiveness.

Here’s why:

• Data-Driven Decisions – Instead of relying on gut feeling, A/B testing lets you compare two design versions (A and B) and measure real user behavior.
• Improves User Experience – You can test which layout, CTA, color, or flow performs better in terms of clicks, conversions, or retention.
• Reduces Risk – A/B testing helps you validate changes before rolling them out to all users, reducing the chance of hurting performance.
• Optimizes for Business Goals – Whether it's sign-ups, purchases, or engagement, you can tie design decisions directly to results.

Bottom line:

If you're serious about improving your app or website’s performance, **A/B testing is a must-have in your design toolkit.

If by remove duplicate users, you mean the same person who has been in both your control and your variant, then yes!

To break down why the answer is yes, think of a website a/b test like a drug study. For the sake of argument, you’ve developing a COVID vaccine.

You have Jim, and Jim receives the placebo + a vaccine. If Jim was cured (or worse died), how do you know which had the affect on Jim? Was there an incredient in the placebo Jim was allergic to OR is the vaccine deadly?

It’s extremely important to remove duplicates and keep the control & variant data pools clean to ensure you can drawer conclusive results in how the 2 (or more) variants perform.

The purpose of a Database is to serve many applications, including those that have not yet been conceived. Thus it must represent business truth. There has to be a way to organize such a structure.

A star schema specifically is for BI. It does not work well for OLTP based applications. For OLTP, we need Relational models.

Modern Big Data crunches way more data than we used to. E.g. Google search, Facebook feed. However these are new use cases. **They fail OLTP**. For example, if your Google search results are 5% different from mine, we don't sue Google. But if ATM machines do this, system failure.

Now imagine the data model of online shopping. One app shows a catalog of goods. One app accepts orders. Another controls inventory. Another is for watehouse picking. All these share data. These cannot be a single star. In fact, for BI on these, there are several stars.

Finally the linked article alludes to getting away from any model. But towards what?

I still value learnjng from traditional modeling, along with new data science practices.

While people from a older marketing background tend to love to test copy, copy tends to be very hit or miss when it comes to driving value (revenue) as opposed to immediate action (clicks). It is important that you are improving total site revenue, and not just short-term action. The other problem is that contextual changes, such as copy, tend to have very short half-lives in terms of return, meaning you might get a 10% lift, but it will only be a 10% lift for a week or so. Spatial changes tend to have much longer half lives, so that a 10% lift might stay a 10% lift for 9 months or a year or so.

Usually the best thing you can do to start out is to either test multiple redesigns of a page against each other, or look to see what the influence of each element on a page is relative to each other. The goal is to set up your testing program so that each test informs the next and that you can make sure that you are applying resources towards the most influential elements based on causal data, not just "intuition" or rates of action. Things like an inclusion/exclusion test are very low cost, high value, and can inform your entire testing series going forward.

I guess the answer to the question heavily depends on the application tier that you are looking at. Usually, big data applications have three or more tiers.

At the presentation and application tier, star schemas are still widely used, because they strike a good balance between space and time complexity of an analytical query.

At the resource tier / back-end , you usually find a database that is fed by a data lake. The data lake does not have one overall schema, but it is usually schema-on-read, and as joins are pretty expensive, we’ll find denormalized tables or just files.

The closer you get to the client tier, the more you see star schemas emerge.

If you are new or learning about data warehouses, then you might have come across the term "Star Schema." To answer your question, we need to define what precisely a "Star Schema" is, what they are used for, the right type for your data warehouse, and the benefits of using one.

A "Schema" is a structure of a database that describes how objects and information are logically connected and grouped in the database. A "Star Schema" gets its name from a database structure whose connection resembles a star with its various points radiating from the centre. To help you get this, let's look at the various components of data warehouses.

Data warehouses are a type of OLAP (Online Analytical Processing) systems. Data warehouses allow users to analyze and access information simultaneously from various sources. Dimensional modeling is an approach used in the design of data warehouses to optimize them for online queries.

In dimensional modeling, there are two types of concepts used which include dimension tables and fact tables.

Fact Tables

These are used to store facts to be used in making business decisions and to be included in reports. The data in these tables are usually in the form of numbers, such as the number of units sold per year. Each fact table will contain a grouping of related facts which are generally used for rapid and easy mathematical manipulation.

Dimension Tables

These tables usually store data that provides more information about facts in the fact tables. These data could be product names, locations, cities, or even customer IDs. Dimension tables reference fact tables in that a fact table usually has primary keys that form their entries on the dimension tables.

As described above, a "Star Schema" has dimension tables and fact tables as its main components. A "Star Schema" is a type of database that has a fact table at the center and multiple dimension tables attached to the center of the fact table. These create a star-like structure and thus the name "Star Schema."

Why are Star Schemas Used?

One reason why businesses prefer the use of "Star Schemas" over other schemas is that they can help produce insightful business reports. Unlike others which are overly complicated, Star Schemas are less complex, and they are easier to understand. They have few joins than other data warehouse schemas, and they are easier to optimize for querying large data sets.

Another factor that makes Star Schemas a favorite for businesses is that they are denormalized. Being denormalized means that the Star Schema contains redundant data records, and this significantly improves the speed and performance of the data warehouse. Specifically, the Star Schema is normalized for its fact tables and denormalized for its dimension tables.

The Star Schema is the most straightforward schema in data warehousing and the benefits of using it for your business include:

1. It has more straightforward queries meaning that the join logic for the Star Schema is simpler than the join logic of another highly normalized transaction schema.
2. The Star Schema offers a simplified business reporting logic than other highly normalized schemas. This is because it is easy to navigate and analyze data compared to different types of schemas.
3. Has efficient Query performance. Star Schemas can provide performance enhancement for the read-only reporting applications than other types of schemas.
4. Faster aggregations, which mean that simpler queries put against a Star Schema can produce an improved performance for all aggregation operations.
5. Can be used to build OLAP cubes efficiently. Star Schemas are best for making OLAP cubes by all OLAP systems. Star Schema and the Major OLAP systems can be used together to utilize the ROLAP mode operation without using any built proprietary cube structure.

Is a Star Schema the right Choice for a Data Warehouse?

The star schema is the right choice for your data warehouse mostly because it offers performance advantages over other types of schemas. But there are a few factors to consider when choosing a Star Schema as your data warehouse option.

If you are building a data warehouse that requires a high level of data integrity, then it is best to choose a normalized schema because, with denormalized schemas, data integrity is weaker.

For end-users, Star Schemas are the best because they are easy to understand and navigate. This means that with a well-designed Star Schema, end-users can easily and quickly analyze multidimensional and large data sets. The Star Schema is also known for its structural simplicity which enables the data warehouse to take less time to load large batches of data.

Another reason why a Star Schema is the right choice for your data warehouse is that they have built-in referential integrity. The referential integrity is enforced as each record in the dimension table has a unique primary key. All the keys in the fact table are legitimately drawn from the dimension tables. Records in the fact table that are not related correctly to the dimension tables cannot have a legitimate key to be retrieved.

Good Alternatives for the Star Schema

While Star Schemas are a highly preferred choice when it comes to data warehousing, they are by no means the only option available. Two other best alternatives to consider when for your data warehouse schema are discussed below.

Snowflake Schema

These are an extension of the star schema model only that they include additional multiple dimension tables. When viewed up close, they look like an intricate structure of the snowflake. The snowflake schema has dimensions that are normalized into multiple related tables.

If you want your data warehouse to be normalized, then the snowflake schema is the best alternative to the Star Schema. One drawback of the snowflake schema is that due to their complex joins; performance is generally worse than that of the star schema.

Galaxy Schema

Galaxy schemas are also known as "fact constellation schemas." They are complex data warehouse schemas that have multiple fact tables which share the same dimension tables. The term galaxy schemas come from the fact that these schemas have numerous or a collection of stars.

The galaxy schema can significantly reduce the size of a data warehouse because the multiple fact tables share the same dimension tables. Galaxy schemas are often used to model various related subjects, unlike the star schema, which focuses on single items.

The simplest and most popular way of organizing information in a data warehouse is by using Star Schemas. However, with the complexity of information and data warehouse integrity needs, the galaxy schema and the snowflake schema are the best alternatives to the star schema. Also, these two alternatives offer additional benefits in a different way for users who need exceptional modeling for their data warehouse.

It is crucial to get the right data-driven insights from your data warehouse. Whether one is using the Star Schema for their data warehouse of a different model, it is always advisable to ensure that the information you need is at your fingertips. Using SQLBot allows one to get automated SQL reports in the email or Slack account to ensure you are equipped with the information you need.

Why do companies use it:

To make a data-driven decision. Given versions A and B, A/B testing helps companies create a decision on which version is better, A or B

Where do companies use it:

Let’s say you want to see an impact of some things like:

• Can a new webpage get more traffic than the older one?
• Do users prefer a status bar at the top or on the side? If it is on the side, is it left or right?
• A new video recommendation algorithm has been developed. How do we determine that it is better than the current one?

How do companies use it:

You can watch this small 10-minute video on A/B testing to understand how companies apply this technique:

A/B testing is an invaluable tool for evaluating the effectiveness of social media designs. It allows businesses to test two different design versions (or other variables like headlines, images, and CTAs) with their target audience to determine which performs better. The key is to isolate one variable at a time to ensure the results are clear and actionable.

For example, you might test two different images for the same post to see which one drives more engagement, or you could experiment with varying call-to-action phrases to find out which one leads to more conversions. The beauty of A/B testing lies in its ability to provide data-backed insights, helping you refine your designs to maximize impact.

Remember, it's not just about testing visuals. You can also A/B test the timing, frequency, and platform of your posts to find what resonates best with your audience. After all, continuous optimization through testing is one of the most effective ways to boost engagement and ROI on social media.

Consistency in testing and interpreting data is key—A/B testing is a process, not a one-time event. Make sure you're constantly tweaking and adapting based on the results to maintain relevance and increase your brand's visibility.

An A/B testing is a kind of experiment, more specifically, an RCT (Randomized Controlled Trial). But, in general, people use the term “a/b testing” for these kinds of experiments when they are applied, for example, on webpages, mobile apps, or digital products, in other words, an online randomized experiment.

A/B tests are very useful and relatively “easy” to do. Roughly speaking, a/b tests work from the segmentation of random groups where each group will receive a different intervention. Randomization allows controlling for observable and non-observable variables, allowing us to infer causal impacts from the interventions (the only variable that should be manipulated in each group).

So, with these kinds of experiments is possible to have an idea about causality, be able to test different kinds of interventions in different contexts, and figure out what works better or not.

In summary, people can do more a/b tests on their online business, mobile apps, or any other platform that has enough traffic and infrastructure to be possible to execute a randomized experiment, to discover what works better. For example, what increases their revenue, conversion rate, and retention or what decreases their churn or complaints.

By doing this, they will be more data-driven and have more chances to take the right decisions about their business.

I want to add one point to the great answers here. A/B testing can only optimize an existing path, it can't come up with radically new directions. A creative human being is needed to do that. Doing that job, designers don't just pull ideas out of thin air, but base them on experience, past testing, and particularly user testing. There comes a point when your web site will be A/B tested to its absolute maximum; there is no way to eke out more gains. But this may be just a local peak. When this happens, watching users interact with the site can give designers and researchers insight into what user needs may still be unmet by the current site. Maybe it's something that was missed before, maybe it's a new market opportunity, maybe the users have changed. Then it might be time to take a risk and try a radically new feature or direction. The new direction might actually decrease metrics in the short term but can then be A/B optimized.

Another way to look at A/B testing vs design is short-term vs long-term goals. A/B testing can optimize the site you have now, in the short term. A designer or UX person is needed to make sure that that optimization doesn't hurt the overall user experience. This would damage the brand long-term. A balance is needed.

It's tricky, but well-structured companies understand there is a place for product managers, UX people, engineers, designers, and both quantitative and qualitative testing - that all contribute to product input. With well-defined roles, those different realms don't need to step on each others' toes ... too much.

1) By collaborating with user researchers.

Some designers are experienced but don't have formal training in HCI or the engineering-y aspects of usability. They'll still often have accurate intuition about what visitors to the site will understand, especially if they're familiar with the audience. However, they may have a harder time articulating why. I think of it as being similar to grammar - you can tell if a sentence sounds "wrong" and can rephrase it correctly but may not be able to easily explain the formal rule or the underlying structure.

If your audience is a group of people who want business decisions to be governed by logic and calculated risk, they may not be satisfied with trusting a single expert's opinion. However, the data you need to support your gut instinct probably already exists. Many, many design problems have already been studied thoroughly and there are lots of research papers, books, etc.
explaining why X will do better than Y, Z is hard to read, et al. The same is true for marketing-focused design decisions.

A/B testing can be helpful or even crucial. It can also be inefficient if you're A/B testing fundamentally flawed designs. People who love data frequently care about efficiency. So prove to them that it can be efficient to rely on already-existing data... and then have a researcher help you acquire that data and discuss it in detail.

We're engineers *and* we love designers :) Some of us even do design work too.

2) Teach yourself to be a researcher.

3) Consider a different design job.

Engineers are rad and they often make very successful sites / software... but a wide variety of other successful companies want designers too. If you really can't stand an environment where you're asked to justify decisions with data, pick one where you won't. (*)

(*) You'll still need charm, building a rappor with your team, skills of persuasion, etc... and in some situations, that can be as challenging as data ;)

It completely depends on the type of company. One of the main distinguishing characteristic is whether the company is making some product that has a lot of users. Consumer web companies are great examples: Pinterest, Facebook, Quora, etc, all have millions of users, and perhaps even the most common and well-known case in the silicon valley.

When you have a lot of users, then you have the opportunity to experiment on fine-tuned details (should we use a blue color button, or a red one? Should we use the old algorithm, or some new one?).

That being said, for enterprise companies that have, say, 100s of users (i.e., clients), it may not be high enough ROI to develop all the infrastructure required for A/B testing. Its a fairly significant investment to get into it because of the backend software that has to be written to support it. It also reflects a change in the way decisions are made — you need to have people in management that consider it valuable to consult data, over intuition, when making important product decisions.

And to be clear, some data scientists act more like software engineers than product analysts, so even at companies where A/B testing is taking place, data scientists may not even necessarily be that involved with it. Its at this point you realize how useless the data scientist label is for describing roles in the silicon valley :-)

Amazon’s Shopping Cart Recommendations is an example of a hugely successful feature that was added based on A/B experiments.

It all started with Amazon.com engineer who had the idea of showing recommendations based on cart items , he argued that doing so would cross-sell more items…

Back at the time, it was widely believed that “nothing should stand between a customer and his credit card”… People argued that it would distract people from checking out and reduce conversion.

So, they ran an experiment, the data relieved the truth and the rest as they say is history.

Very important! Usually a data scientist works with offline experiments with proper metrics: misclassification rate, forecast errors, recall and precision, etc. In this case a data scientist neglects effects those appear while user interacts with your system.

A good example - recommender systems. It’s impossible to predict the behavior of a user by offline experiments completely because a new algorithm will influence on user’s behavior. From my experience 9 of 10 positive offline metrics will not yield positive results in online experiments.

See a very interesting paper about it: Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics

There are some well-known places to start that direct marketers have known for years. I'll start with the caveat that it depends on your goal with the page (e.g., opt-in vs. signup vs. sale, etc).

Anyway, I'll give you some examples.

For instance, when we think about testing a sales page or landing page, we spend at least 30% of our time on the headline.

We spend a massive amount of time thinking about the offer, including what we're promising, how we deliver it, the price, the tiers, the guarantee, etc.

Another example: I have a marketing friend who ran 2 split tests on headlines, and one of his headlines produced 4x the revenue.

REVENUE, not opt-in conversions.

Despite the hyped-up case studies that we always see floating around, you will almost never get huge revenue gains from changing the color of your signup button.

Instead, I suggest focusing on large things like:
- Offer (VERY VERY IMPORTANT): Is your offer compelling? Can you present it a different way? Can you use time or other persuasive elements to cause your desired result?
- Headline (VERY IMPORTANT)
- Copy: Are you using words your prospects identify with? Is it clear? Crisp?

It depends for each situation, but if I had a simple web-app homepage that I was testing, I would spend my time as follows:

50%: Offer (e.g., what are you promising them? Hint: It should not be an app. Nobody cares about your stupid app. It should be a SOLUTION TO THEIR PROBLEMS)
40%: Headline
10%: Random fun tests

Over time, I have cut back dramatically on the "random fun tests" because, while it may be fun to run a 3-day split test on the size of my fonts and get a 6% increase, it's more fun to spend a month doing really hard work on a compelling offer/strategic change and see a 500% change.

Star schema is a simplest form of dimensional data model where the data is organized into facts and dimensions. One fact table connects to several dimensions.

Fact table: A fact table is a highly normalized table which contains measures (measures are facts about your business process. ex: $GrossBookingAmt, Qty etc), keys (reference keys aka foreign keys to the dimensional tables), primary key (which could be formed using a combination of FKs aka composite key or unique key if present) and degenerate dimensions ( a dimension at it's atomic form but needed for analysis. ex: booking ID or Itinerary Number)

Dimension table: Dimensions contains data about the data. Can act as a conforming dimension by being a common dimension to fact tables, that way connecting the facts.

Could also act as role playing dimension. Role playing dimensions are nothing but alias dimensions where the information is about the similar entities but might differ based on the "role" or "action" of a dimension. And are created to support a reporting/cube infrastructure.These might be present as views in your database ex: information about an agent who made your flight booking can be stored in a "BookAgentDim". And information about the agent who updated your booking can be stored in "UpdateAgentDim". Of course, again the fact table would have the respective foreign keys.

Ex of a dimension: Employee Dimension - would contain information about the employee like, the employee name, employee ID, role, start date etc.

a simple star schema

Multiple fact tables with conforming dimensions (as marked)

Refer to Raphl Kimball, dimensional data modeling

Images are from google

Here are 5 of the top mistakes I’ve seen in the wild:

Mistake #1: Not testing your A/B testing setup

Your A/B test delivery and measurement tools are a piece of software prone to issues just like any other. If it is unreliable or not working as expected, for some reason, its output will not be trustworthy. A central function of A/B tests — to serve as a last layer of defense against releasing business-damaging user experiences — would be partially or completely compromised if the A/B testing rig is not working to specification. However, many practitioners fail to realize this, or the checks they perform are insufficient.

Mistake #2: Measuring the wrong metric

Oftentimes A/B tests will default to using conversion rate or a similar metric as primary outcome measure. The Issue is that oftentimes such a metric cannot answer the business question being put to the test. This is certainly the case when there is variance in the business value attached to conversions, such is the norm in most of e-commerce and even in some lead-gen scenarios.

Mistake #3: Using two-tailed tests

Most business questions we face in online A/B testing are of the form “we want some guarantee that experience B is better than A as measured by metric X”. These translate properly into a one-sided hypothesis and thus a one-tailed test. We rarely, if ever, ask the question as such “we want some guarantee B is not the same as A, whichever way”, which would indeed require a two-tailed test. Even more rarely are we prepared to pay the price to get the same level of precision for such an answer, as opposed to the previous one.

The Issue is, therefore, that the statistical model of a two-tailed test is not aligned with the business question at hand, however it is what is often used in many software tools, either explicitly, or by default. This results in many practitioners committing this mistake, knowingly or not.

Mistake #4: Overestimating the sample size for A/B/n tests

The issue with tests with more than one variant is that often the sample size per test group will be calculated for a simple A/B test, and then multiplied by the number of tested variants plus one, to get the sample size for the A/B/n test.

Consider this scenario: A/B/n tests with 4 variants vs. control (A/B/C/D/E). A naive calculation would start by estimating that 24,903 users would be required per group for an A/B test. Therefore, for an A/B/C/D/E test the total number of users is simply 5 x 24,903 = 124,515.

However, in reality, only 95,880 users (total) are needed for the A/B/C/D/E test with these parameters, or 23% less than the naive calculation.

This mistake happens regularly since many calculators (both free and paid) have poor or no support for A/B/n tests..

Mistake #5: Unaccounted for peeking with intent to stop

While there are good statistical methods for evaluating data in a sequential manner (search for “agile a/b testing” for a method tailored to online A/B testing), oftentimes a very simple statistical model will be used, which only permits one data evaluation for its error guarantees to hold. If, instead, the test outcome is evaluated at several points in time, the model is no longer adequate, resulting in its nominal statistics being different than the actual ones.

For example, a nominal p-value of 0.01 reported from such a test may actually be 0.06 or 0.2. Similarly this holds for confidence intervals and most Bayesian estimators.

Most of these issues are covered in detail, including the likely causes behind them are, as well as proposed solutions, in this article: Top 5 mistakes with statistics in A/B testing (from where most of the info was sourced).

Try searching on "scaling A/B testing". You'll find some interesting articles.
Netflix, for example, does hundreds of A/B tests per year pushing in excess of 2.5 million css/js packages. They use Node.js for teir A/B testing.

You'll also find an interesting paper by experimentation platform.

Controlled Experiments At Large Scale

The Behavioral Insights Team had a trial where they split tested people who had renewed their car tax online to one of eight messages to encourage joining the organ donor registry.

This is how they performed.

It is estimated that with the conversion rate of the best performing variant compared to the worst performing, there would be 90,000 people more in the organ donor register in a year

Footnotes

Good database schema designs balance simplicity, scalability, and efficiency. Here are some examples:

• E-Commerce: Separate tables for Users, Products, Orders, and OrderDetails to avoid redundancy, with proper indexing on frequently queried fields like product_id and user_id.
• Social Media: Tables like Users, Posts, Comments, and Likes with clear relationships and indexed keys for performance.
• Healthcare: Patients, Doctors, and Appointments linked efficiently while maintaining data security and compliance.
• Booking Systems: Separate Users, Rooms, and Bookings tables with indexed date fields for faster searches.

There are many tools available to help design and visualize database schemas. Personally, I use dbForge Studio for SQL Server, which makes creating and analyzing every SQL schema much easier with its intuitive interface and features.

Hi, I am Sparsh, a lifelong A/B testing enthusiast and also the Co-founder and CEO of VWO - an industry-leading testing and experimentation platform.

In their sunset announcement, Google recommends 3 A/B testing providers: A/B Tasty, Optimizely, and VWO.

Although all 3 tools have been around for the better part of a decade, what tool you end up choosing should depend on your needs and budget.

I will attempt to be unbiased here and share VWO's strengths compared to the other two and explain for whom VWO might be a good fit.

If you're just getting started on your experimentation journey and are not very sophisticated/strategic in your experimentation program, I would suggest going with a tool that is the cheapest.

VWO offers a generous free plan for testing while the others don’t. This plan includes:

• Unlimited concurrent tests for upto 50k monthly visitors
• An easy-to-use no-code editor
• Easy integration with your existing stack including Google Analytics, and Google Ads
• Capability to run experiments on websites built on Single Page Application frameworks without any manual overrides
• Solid support with 99% CSAT

On the other hand, if you're a mid-market/enterprise company with a good enough traffic and experimentation roadmap, I would urge you to explore not one tool but rather 3 tools:

• A heatmap/session recording tool
• An A/B testing tool
• A CDP (for integrating first-party data)

VWO offers an integrated platform with a heatmap/session recording tool, an A/B testing tool, and a CDP that you can manage via a single dashboard with insights and data feeding into each other.

While Optimizely offers a robust data platform to enhance digital experiences, AB Tasty does not have the same.

For visitor behavior analytics, in the case of AB Tasty and Optimizely, you need to add multiple code blocks and onboard additional tools like FullStory or Hotjar. Multiple codes can add to your website loading time and page load impact is an important factor to consider when choosing an A/B testing tool.

As per data on Github, VWO is the fastest experimentation platform in the CRO industry (at least 2X faster than Optimizely and 4X faster than AB Tasty).

You must also check what kind of support is available for training and troubleshooting. When you run into issues, you want the support team to work with you like a partner, and not simply direct you to a help article. With VWO, users get unlimited email, phone, or chat support depending on their plan at no extra cost.

Finally, you want your tool to debunk test data for you. With VWO, measuring the impact of experiments and finding business insights is intuitive, error-free, and works in real time. You can slice and dice experimentation results with predefined or custom segmentation on the go.

I hope this information helps you. If you are an existing GO user and choose to go with VWO, we have a simple Chrome plugin that will help you transfer your campaign data to VWO within 30 seconds.

I’ll answer this question from the perspective of improving a (web/software) product. A/B tests exist in other context, but I’m pretty sure that’s where the asker is coming from.

The concise answer isthat you should run an A/B test when the cost of doing so is outweighed by the expected improvement in the decisions you’ll make. Determining that tradeoff involves taking into account a lot of factors though.

On the upside you get to make better decisions. You get a glimpse into a potential alternate universe and then get to decide whether you like it or not. For big enough decisions, this is certainly worthwhile. Even experiments you’re running that you don’t envision as a final state can help you make decisions about what to build next. The alternative is just guessing what’s best, and if you run enough experiments you’ll learn human intuition is far from perfect.

This weakness of human intuition is especially profound as the complexity of what you’re changing increases. Engineers can refactor a huge back-end system and expect the user will be unable to notice. They could be right, but I’ve seen enough examples of unintended side effects to think it’s worth checking.

Let’s talk about the costs now. A/B tests are not free. They take developer time to set up, and analyst time to evaluate. Both of these quantities can be reduced by having good experimental tools in place. Furthermore, experiments take clock time to run. During that time there is extra code to maintain and ambiguity as to what the final state of the world will be, both of which will reduce development velocity.

Beyond that there is likely a cost to whomever your exposing to your experiment. Each experiment has a pretty high chance of being a bad experience for the user, including a tail risk of being very bad. (Sadly, experiments are almost never unexpectedly very good for users). Also, if the experience is mediocre and not shipped or needs to be modified you’re also thrashing your users, which can cause long-term confusion and frustration. This effect is hard to quantify but certainly is present and probably accumulates if experience changes too often.

Taking that into account, some experiments just aren’t worth running. In many cases you can’t plausibly expect the differences to matter (e.g. changing the font on a button buried deep in your product). Or maybe you can’t reasonably expect to actually get useful information from an experiment because the error bars on the metrics you care about are too wide. This is something you can know beforehand, and I’ve seen a lot of time wasted because someone didn’t bother to.

Ultimately, trading off all these factors against each other is an imperfect science, but if you at least think it through a bit you’ll be more efficient with your resources.

Flat-file database - Wikipedia

Thank you for your - Question.

and have a nice day everyone.

Let me try to answer in non-technical terms.

• Star Schema is a method of information organization, storing reference information separately from normal data. The reference information is stored in multiple dimension tables, usually one table per subject say products, customers, geographic information and so forth.
• The normal data table is called the fact table; the records in the fact table would have pointers (called foreign keys) to the appropriate dimension record. For example product id would point to a product row in the product dimension table, customer id would point to a row in the customer table
• Wikipedia calls the fact table as “measurable, quantitative data about a business” and the dimension table as “related descriptive attributes”
• That way, the reference information occurs only in one place, thus achieving data integrity, storage optimization and faster queries.
• This organization is most frequently used in Data Warehouses and Data Marts for Business Reporting and Analytics.

Yes there is no doubt behind this, well after doing A/B testing we found better one with time. A/B testing is that kind of key which is really helpful to open the door of same app with new features and applications. A/B testing is 100% useful for approx overall mobile apps and ever lots of article, contents are available over internet which can prove these points easily. I think A/B testing is the only way to truly understand how the changes can be happen in an app.

A/B testing is essential for optimizing web and mobile site or in other word we can say that it very important for finding the best result or finding the best one app in between two mobile app.

Well you also need to understand why A/B testing is important for mobile app.

1. When you want to increase engagement of users in your site.

2. When you are looking to increase conversion rate.

3. When you are looking to reduce bounce rate of your site.

4. When you are looking to take minimum risk.

5. When you are looking for learning and continuous improvement of your site.

6. When you are looking for doing new experiment and find out best result.

Hope you are cleared now why it is important.

Thanks