Statistical significance means that the results of a test are unlikely to have occurred by random chance.

In marketing and A/B testing, it shows whether the difference between two versions is real or just luck.

A result is called statistically significant when the probability of error is very low, usually less than 5% (p-value < 0.05). It helps businesses make confident decisions based on data rather than intuition.

Standard error, in very simple terms, it tells us how accurate a sample average is likely to be.

A small standard error means the sample is close to the real value, while a large one means more uncertainty. For marketers, it helps judge how reliable test results are before making decisions based on them.

A squeeze page is a simple landing page designed to capture visitor information, usually an email address, in exchange for something valuable like a free guide, webinar, or discount. Unlike long sales pages, squeeze pages are short, focused, and avoid distractions.

Their only goal is to get a visitor to sign up or subscribe. Marketers use squeeze pages to grow email lists, build leads, and nurture relationships that later convert into paying customers.

Split-URL testing is a testing method where users are sent to completely different web page URLs to compare performance. Unlike standard A/B testing, which changes small elements like buttons, split-URL testing compares entirely different designs or layouts.

It’s often used for major redesigns, landing page strategies, or testing large content changes.

Because the differences are bigger, results can show clear insights. However, it requires more development resources and careful tracking of user behavior.

Split testing for pricing is when businesses show different groups of customers different prices for the same product to see which price drives more sales or profit.

This helps companies understand the balance between customer willingness to pay and business revenue.

The method must be handled carefully to avoid upsetting customers who notice different prices. When done ethically, it gives strong insights into customer psychology and price sensitivity.

Split testing, also called A/B testing, is a way to compare two or more versions of a webpage, email, or ad.

Visitors are randomly shown different versions, and their behavior is tracked to see which version performs better. It helps businesses make data-driven decisions instead of relying on guesswork.

Split testing can test headlines, images, CTAs, colors, layouts, or offers. It’s one of the simplest and most reliable ways to improve marketing performance.

Social proof is the psychological idea that people copy the actions of others when deciding how to act.

In marketing, it shows up as testimonials, ratings, reviews, case studies, or 'as seen on' badges. People trust other people more than brand claims, so showing evidence of happy customers builds credibility.

Social proof helps reduce doubts and improves conversions. It’s especially powerful when your target audience sees others similar to them making the same choice.

Simpson’s Paradox happens when a trend seen in different groups reverses or disappears when those groups are combined.

It’s a statistical effect that can mislead analysis if not checked. This usually occurs because hidden or confounding variables are influencing the outcome.

For marketers or analysts, it highlights why data segmentation matters. Without breaking results into relevant groups, brands may make wrong conclusions and base important business decisions on inaccurate findings.

Session recording is a tool that captures how users interact with a website in real time. It records clicks, scrolls, typing, and navigation paths.

Businesses use it to identify user behavior patterns, find friction points, and improve the overall website experience.

These recordings are anonymized to protect user privacy.

Server-side testing is an experiment where test variations (like different page layouts or content) are created and delivered directly from the server before reaching the user’s browser. It gives more flexibility and accuracy compared to client-side testing. It’s commonly used for complex experiments like checkout flows or personalization.