Statistics / A/B Testing
A/B Testing
A/B testing sounds simple — show two versions, see which wins — but most of what makes a test trustworthy happens before and after the obvious part: calculating the right sample size, avoiding the urge to peek early, and correctly interpreting what "significant" actually means. Done properly, it's the most reliable way to know a change caused an improvement, not just coincided with one. We help you design tests that are actually valid, and analyze results without the common pitfalls that quietly invalidate them.
This Is For You If...
You're planning an A/B test and don't know how many users or how long you actually need to run it
You've run a test and got a result, but aren't confident it's statistically meaningful
You keep checking results early and want to know if that's actually a problem
You need to properly analyze a completed test, including checking for issues that could invalidate the result
Which Stage Are You At?
Ask Yourself | Points Toward |
Are you still planning the test — what to change, what to measure, how to split users? | Experiment Design |
Do you know your baseline conversion rate but not how many users you need? | Sample-Size Calculation |
Have you got results and need to know if the difference is real or just noise? | Statistical Significance |
Is your specific goal improving a conversion or completion rate? | Conversion-Rate Testing |
Do you have completed test data that needs proper, rigorous interpretation? | Experiment Analysis |
These are sequential stages of the same process — good experiment design leads into sample-size calculation, which determines how long to run the test, which feeds into significance testing and final analysis. Tell us where you are in that process and we'll help with what's next.
A/B Testing Services We Offer
Experiment Design
Experiment design is the foundation everything else depends on — defining a clear, falsifiable hypothesis before the test begins, choosing a primary metric that's directly tied to that hypothesis and sensitive enough to detect a change, randomly assigning users to control and treatment to ensure comparable groups, and deciding what to change (ideally one meaningful thing, not several at once). We help you design a test that will actually produce a valid, interpretable result, not one that looks reasonable but has a flawed setup baked in.
Sample-Size Calculation
Sample-size calculation determines how many users each group needs before the test begins, based on four inputs: your baseline conversion rate, the minimum detectable effect (MDE) you care about, your significance level (alpha, typically 5%), and your desired statistical power (typically 80%). Calculating this properly in advance is one of the most important steps in experiment design — it prevents both stopping too early (producing false positives) and running longer than necessary (wasting traffic on an already-conclusive test). We help you calculate the right sample size for your actual baseline and the effect size that matters to your business.
Statistical Significance
Statistical significance tells you whether the observed difference between your control and variant is unlikely to have occurred by random chance alone — but it's a specific, technical claim, not the same as "important" or "meaningful," since even a tiny, practically irrelevant improvement can be statistically significant with enough traffic. We help you correctly compute and interpret significance, and just as importantly, distinguish statistical significance from practical significance for your actual business decision.
Conversion-Rate Testing
Conversion-rate testing applies A/B testing specifically to improving a conversion or completion metric — sign-ups, purchases, click-throughs — and requires particular care in choosing a primary metric that's sensitive enough to detect real change within a practical test duration (metrics like revenue or long-term retention are often too noisy or slow to serve as a good primary metric). We help you choose the right metric and design a test genuinely capable of detecting a meaningful conversion change.
Experiment Analysis
Experiment analysis is the rigorous interpretation of completed test results — checking statistical significance properly, but also examining secondary metrics for red flags, checking whether the effect holds consistently across user segments, and confirming the test wasn't compromised by common issues like early stopping or an inadequate sample size. We help you analyze your results thoroughly and arrive at a defensible ship/iterate/kill decision, not just a surface read of the headline number.
Not sure which stage you're at, or need help across the whole process from design through to a final decision? That's the normal, complete engagement — tell us about the test you're planning or have run, and we'll help with the right next step.
What We Need From You
What to Share | Why It Helps |
The change you're testing and your hypothesis for why it will help | The hypothesis should exist before the test is designed, and shapes metric choice and sample-size calculation |
Your baseline conversion rate (or the metric's current value) | A required input for sample-size calculation |
Your available traffic/user volume | Determines how long a properly-sized test will realistically take to run |
Your existing test data, if the experiment has already run | Lets us analyze results rather than design from scratch |
Any secondary metrics you're also tracking | Important for checking for red flags beyond just the primary metric |
Your deadline | Helps us scope the right turnaround |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Checking results daily and stopping as soon as you see significance | Stopping early based on repeated peeking significantly inflates your false positive rate — a well-documented and common A/B testing pitfall | Calculate sample size in advance and let the test run to completion before making a decision |
Testing several changes at once and calling it an "A/B test" | Changing multiple things at once makes it impossible to know which change actually drove any observed difference | Change one meaningful thing at a time for a true A/B test, or explicitly design a multivariate test if you need to test several changes |
Choosing revenue or long-term retention as your primary test metric | These metrics are often too noisy or too slow to change within a practical test duration, making the test underpowered to detect real effects | Choose a primary metric directly tied to your hypothesis that's sensitive enough to move within your test's realistic duration |
Treating statistical significance as proof the change is worth shipping | A statistically significant result can still be a practically trivial improvement not worth the cost of shipping | Evaluate both statistical significance and practical significance (is the effect size actually meaningful?) before deciding |
Running a test for a fixed calendar time instead of the calculated sample size | Doesn't account for actual traffic patterns, and risks stopping before or well after the point of a valid, reliable result | Base test duration on the calculated required sample size and your actual traffic volume, not an arbitrary time window |
Ignoring segment-level results when the overall result is significant | An overall positive result can mask the change actually hurting a meaningful subset of users | Check whether the effect holds consistently across relevant user segments, not just in the aggregate |
How It Works
1. Tell us where you are
Share whether you're designing a new test, calculating sample size, or analyzing completed results
2. We work through the relevant stage rigorously
We design the experiment, calculate the proper sample size, or analyze results with attention to the common pitfalls that invalidate tests
3. We check for issues that could undermine validity
We look for early-stopping risk, inadequate power, metric noise, or segment-level red flags, not just the headline number
4. We interpret results in practical terms
We distinguish statistical from practical significance, and connect the result to an actual ship/iterate/kill recommendation
5. We explain the reasoning clearly
You get a plain-English explanation of the methodology and the result, useful for a class, a stakeholder presentation, or your own decision-making
Why Codersarts
Most A/B testing mistakes aren't in the test itself — they're in the setup (an underpowered sample size, a poorly chosen primary metric) or the interpretation (peeking early, confusing statistical with practical significance). We build experiments properly from the start and analyze results with real statistical rigor, for real product and marketing teams making decisions based on test results. We're honest when a result isn't actually conclusive, not just when it's convenient to say so.
Turnaround Time
Experiment design and sample-size calculation are typically delivered within a day or two. Full experiment analysis of completed test data is usually turned around within a few days. Research and launch deadlines are prioritized accordingly — let us know your timeline upfront.
FAQ
How do I know if my sample size is actually big enough?
We calculate this based on your baseline rate, the minimum effect size you care about, and your desired significance and power — happy to walk you through the exact numbers for your situation.
I've been checking my test results daily — has that ruined the test?
Not necessarily, but repeated peeking increases the risk of a false positive if you stopped early based on what you saw. We can help assess whether your specific situation is still valid or needs a different analysis approach (like a sequential testing method).
What's the actual difference between statistical and practical significance?
Statistical significance means the result probably isn't due to random chance. Practical significance means the effect is actually large enough to matter for your business. A result can have one without the other, and we help you evaluate both.
Do you use specific tools for this?
Yes — we work with Python (SciPy, statsmodels), R, and common experimentation platforms, depending on your existing workflow.
Is this for coursework, or only real product/marketing experiments?
Both — we support academic coursework in experiment design and statistics, as well as real product, marketing, and growth experiments.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Planning or Analyzing an A/B Test?
Whether it's research or a real product or marketing experiment — get a test design and analysis you can actually trust.