🧪 What Is A/B Testing?

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Hey Learners! 📚 They say you learn something new every day, and that’s true.. if you’re a Waivly Learn reader.

It’s that time of the day where you get to learn something brand new or level up your knowledge and skills on a topic you’ve already started to explore.

Today, we’re learning about A/B testing. Let’s dive in!

TODAY’S LESSON

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What Is A/B Testing?

A/B testing is one of the simplest and most powerful tools in a builder’s toolkit. It’s exactly what it sounds like: testing two versions of something—A and B—to see which one performs better. Whether it’s an app button, a signup page, or a subject line in an email, A/B testing helps you base decisions on data, not just gut feeling.

Here’s how it works: you split your audience into two random groups. Group A sees the original version (the “control”), and Group B sees the new variation. You then compare how each group responds—clicks, conversions, time spent, whatever metric matters most. The version with better results wins.

What makes A/B testing so effective is its simplicity. You’re not guessing. You’re running small experiments that reduce risk and increase your chances of making the right call. It’s how companies fine-tune their user experience over time without having to make huge, sweeping changes all at once.

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But A/B testing isn’t just about tiny tweaks. When used well, it can lead to big breakthroughs. Maybe a small change in copy improves signups by 20%. Maybe a new onboarding flow cuts churn in half. These wins stack up—and over time, they compound into serious growth.

That said, not everything should be A/B tested. You need enough users to get meaningful data, and the test has to run long enough to trust the results. Testing for the sake of testing can lead to noise instead of insights. Focus on decisions that actually move the needle.

And remember—A/B testing doesn’t replace intuition or creativity. It supports them. It helps you validate ideas, challenge assumptions, and get closer to what your users actually want.

In short: A/B testing helps you learn faster, build smarter, and grow more intentionally. Just one test at a time.

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Hey Learners! 📚 They say you learn something new every day, and that’s true.. if you’re a Waivly Learn reader.

It’s that time of the day where you get to learn something brand new or level up your knowledge and skills on a topic you’ve already started to explore.

Today, we’re learning about why AI needs good data. Let’s dive in!

TODAY’S LESSON

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Why AI Needs Good Data

If AI is the engine, data is the fuel. And just like you wouldn’t put dirty gas in a sports car, you don’t want to train an AI model on messy, low-quality data. The performance of an AI system—how smart it seems, how accurate its responses are, how useful it becomes—depends almost entirely on the quality of the data it's fed.

Good data doesn’t just mean a lot of it. It means accurate, relevant, unbiased, and well-labeled data. For example, training a facial recognition model on photos that are mostly of one demographic will skew its accuracy. The model might perform well for some people and terribly for others, not because the algorithm is bad—but because its foundation was flawed.

AI systems learn patterns from whatever you give them. If the training data includes typos, gaps, or inconsistencies, the model will internalize that noise. You might end up with a chatbot that confidently answers questions... incorrectly. Or a recommendation system that feels off because it’s drawing from outdated or irrelevant data.

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Superhuman AI

This is why data cleaning and curation are just as important as the algorithm itself. Before any AI can be useful in the real world, someone has to make sure the input it’s learning from is solid. That means removing duplicates, standardizing formats, fixing errors, and making sure the data actually reflects the problem it’s trying to solve.

Context also matters. A dataset of restaurant reviews might be great for sentiment analysis—but not if you’re trying to build a voice assistant. The type of data you collect should always match the goal of the model. No matter how advanced the AI, it can’t compensate for input that doesn’t make sense.

At the end of the day, the saying “garbage in, garbage out” has never been more true. The smarter AI gets, the more important good data becomes. Want AI that feels truly intelligent? Start by feeding it something worth learning from.

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Become a Learn Plus member

As a Waivly Learn Plus member, you gain exclusive access to:

• Exclusive access to courses 🎓

• Members-only lessons 📖

• Private community access 🌐

• Personalized learning assistance 🤝

• Advanced professional development training 🚀

• And much more 🎉

Waivly Learn Plus is designed to elevate your growth through exclusive access to courses and members-only lessons that target essential skills and knowledge. With advanced professional development training, you'll gain practical tools to accelerate both personal and professional success, empowering you to continually expand your expertise.

Alongside our premium content, you'll be part of a private community of driven learners and experts who share your commitment to growth. Here, you can connect, exchange insights, and find support as you work toward your goals. Join Waivly Learn Plus today to transform your learning journey with the resources and connections you need to thrive!

UNTIL NEXT TIME

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That wraps up today’s Waivly Learn lesson

We hope you enjoyed today’s lesson 🙌 Let us know if there’s a topic that you want to learn about that you haven’t seen from us. Want to share feedback or suggestions? Respond to this email‏ - We read every reply! Make sure to follow us on X, TikTok, YouTube, Instagram, and LinkedIn for more from us each day - We’re @Waivly everywhere!‎‎