What is this even for?

Validity: Validity refers to how accurately a study measures what it is intended to measure. In other words, it asks whether researchers are actually testing the concept or outcome they claim to be investigating. A study can produce consistent results, but if it is measuring the wrong thing, those results are not valid. Researchers consider several types of validity, including internal validity (whether the study design allows researchers to confidently attribute results to the intervention being tested) and external validity (whether the findings can be generalised to other populations or settings). High validity helps ensure that research findings are meaningful, accurate, and useful in the real world.

Bias refers to a systematic error or deviation from the truth that can occur during the design, conduct, analysis, or interpretation of a study. Unlike random errors, which occur by chance, bias consistently influences results in a particular direction and can lead to inaccurate or misleading conclusions.

A placebo is a harmless substance or treatment that does not contain any active therapeutic ingredients. Most often its something like a sugar tablet. In clinical trials, placebos are often given to a control group so researchers can compare their outcomes with those receiving the actual treatment. This helps scientists determine whether any observed effects are due to the treatment itself or to psychological factors such as participants' expectations, known as the placebo effect.

Ethics are at the core of everything scientists do. And there are many different ethics at play.

When scientists measure something using a sample (a smaller group taken from a larger population), the result they get is an estimate, not a perfect measurement.

When scientists run an experiment, they want to know whether their results are real or if they might have happened just by chance.

Sample Size:A sample size is the number of people, items, or data points chosen from a larger population to take part in a study.

This is the maths bit. Everything about the results from an experiment is usually turned into numbers in some way, whether that’s tumour size, blood pressure levels, reaction times, test scores, or survey responses. Assigning numerical values to results allows scientists to measure change in a clear, structured way, making it easier to compare results between groups, across time, or under different conditions.

You’ve probably heard someone say something like, “Studies show that X is linked to Y,” and it can sound pretty convincing. But in science, the word linked carries a very specific meaning, and it doesn’t always mean one thing is causing the other. One of the most common and most misunderstood concepts in research is correlation. It sounds intimidating because it’s a statistics term, but at its core, it’s simply a way scientists measure how two things change alongside each other.

Have you ever read a headline announcing a “breakthrough treatment” or a study claiming something dramatically improves health, learning, or behaviour and wondered how scientists actually know that change was caused by what they tested? In research, proving that something works isn’t just about seeing improvement, it’s about showing that the improvement wouldn’t have happened. That’s where one of the most fundamental building blocks of scientific experiments comes in: control groups and experimental groups. These two groups allow researchers to separate real effects from coincidence, natural change, or outside influences and without them, it would be almost impossible to trust the results of most scientific studies.

The basis for everything we do! While it seems strange, it was important to explain a couple of other factors before explaining what an experiment actually is. An experiment is a controlled procedure that is carried out to test a hypothesis. This typically involves manipulating one or more variables to observe the effects it has.

Variables are anything that can either be measured or controlled in an experiment. There’s three types of variables; Independent, Dependent and Controlled. What these mean varies from lab to lab. 

Starting an experiment, there is always a question being asked-