Understanding how confounding variables and bias can mislead experimental results is essential for drawing valid conclusions from data. A confounding variable is an outside influence that changes the effect of a dependent and independent variable. When confounders are present, it becomes unclear whether the observed outcome is due to the experimental treatment or the confounding variable. For instance, if you test a new website layout and notice increased sales, but all your mobile users saw the new layout while desktop users saw the old one, device type becomes a confounder — it is impossible to tell if sales increased because of the layout or because mobile users tend to buy more.

Bias in experiments refers to systematic errors that can skew results away from the true effect. Bias can arise in many forms, such as consistently selecting certain types of participants (selection bias), mismeasuring outcomes (measurement bias), or losing participants in a non-random way (attrition bias). Suppose you run a drug trial and only include healthy volunteers; your results may not generalize to the broader population, illustrating selection bias. If participants self-report their symptoms, and some exaggerate or downplay them, measurement bias is introduced. If people who feel worse are more likely to drop out, attrition bias can distort the findings.

A clear understanding of bias types is crucial for designing robust experiments. The following table summarizes common types of bias, gives practical examples, and suggests remedies:

By identifying and addressing these biases, you can strengthen the credibility of your experimental findings.

Designing an experiment starts with defining its core components: experimental units, factors, and responses. The experimental unit is the smallest division of the experiment that can receive a treatment independently. The factor is any variable that is controlled and systematically varied by the experimenter. The response is the outcome measured to assess the effect of the factor.

Consider a website experiment where you want to test if changing the color of a "Buy Now" button affects purchase rates. Here:

• The experimental unit is a single website visitor;
• The factor is the button color (e.g., red vs. blue);
• The response is whether the visitor completes a purchase.

Structuring your experiment this way allows you to isolate the effect of the factor and minimize the influence of confounding variables and bias.