Before I answer that, let me explain why every NLP intervention needs a “control”, and what science teaches us about measuring change… In science, nothing is taken at face value. If you want to know whether something works, you don’t just test it — you compare it. You measure it against a control. A control is the baseline, the “normal,” the standard set of conditions you already understand. Without it, an experiment becomes guesswork. You might get a result, but you have no idea whether that result is meaningful. And this principle applies far beyond laboratories and test tubes. It applies directly to NLP, personal change work, and the question people love to ask: “Does NLP actually work?” Let’s break this down. The Science Analogy: Why Controls Matter Imagine I’m testing a new polymer additive in asphalt in my business (MacRebur Limited). I want to know whether a new additive improves strength, flexibility, durability — whatever my goal is. So I create two samples: • Control asphalt — the standard mix that’s always used • Experimental asphalt — the same mix, but with my polymer additive included I test both to a set of criteria and standard. I compare the results. If the polymer works, my experimental asphalt should outperform the control. If I didn’t have the control asphalt, I’d have no idea whether the polymer made any difference at all. Maybe the asphalt was always that strong. Maybe it was always that weak. Without comparison, the experiment is pointless. NLP Works the Same Way… When we use NLP tools — anchoring, reframing, submodalities, timeline work, language patterns — we are essentially running an experiment on human experience. But here’s the key: You cannot measure change unless you know what the experience was before the intervention. This is why NLP questions often presuppose comparison: • “How do you feel differently now, compared to before?” • “What’s different?” • “What’s changed?” • “What’s shifted?” These questions aren’t fluffy. They’re the NLP version of a scientific control.