In this video, we're going to circle back to the concept of validity. So remember, previously we had talked about validity as it applies to things like psychometric assessments. But when we are thinking about the validity of a study's design, or for example, an experimental design, we consider two different types of validity, and those are internal and external validity. Internal validity is essentially an indication of how well a study is controlling potentially confounding factors. So basically, you know, how well is this experiment designed, and how confident can we be that the result that we're seeing is the effect of the independent variable on the dependent variable.
Some things that can, kind of boost or increase internal validity in a study are things like random assignment, which we know is going to increase equivalency among groups, give us, you know, more control there. Using things like reliable psychometric assessments or, you know, assessments with good construct validity. So if we're just using really well established tools in the field, as well as things like making an experiment double-blind. All of these things are going to be basically reducing confounding variables and that can make us feel more confident that what we're seeing again is the effect of the IV on the DV.
Whenever I think of internal validity, I always just think, like, internal as in, like, inside of the experiment. So is everything internal to, or everything inside of the experiment working the way that it should? Is there a nice strong experimental design? And if so, you have stronger internal validity. Now, in contrast, we have external validity, which is basically the extent to which the findings can be generalized to the population.
So basically, you know, can we take the result from our sample and confidently say that it applies to the entire population? And external validity can be strengthened by things like random sampling. Remember with random sampling we're going to end up with a sample that better reflects the population typically, as well as creating, you know, study conditions that reflect the real world as much as possible. So to give you an example of that, like, I've worked with kids for most of my career, and I worked in one lab where the lab looked like a doctor's office almost. It had, like, tile floors and fluorescent lights and all that, and kids would be really nervous when they came in.
Sometimes they'd act a little bit weird. But in a different lab I worked at, we made our lab look like someone's living room. So it had, like, a plush carpet. It had big comfy couches and chairs and potted plants and pictures on the walls, and kids would walk in and be like, whose house is this? Like, where are we?
But they'd be really comfortable. You know, they'd get up on the couch and just be chit chatting, and they act much more naturally because they were so much more comfortable. And because they were acting naturally, we could kind of be a little bit more confident that our results were going to generalize to how they would act in the real world. Right? So doing these types of things are going to boost your ability to really be confident that your findings can be applied to the broader population.
Now I do want to be really clear that not every single study necessarily has to have good internal and external validity all the time. Some studies are a little bit stronger in their internal validity, some studies are stronger in their external validity. There are many different reasons, you know, why you might be stronger in one versus the other. The research might have different purposes, different topics. Sometimes it's easier to have internal versus external.
I just want to be clear that, you know, you don't have to be judging every study and expecting them to have, like, incredible internal and external validity all of the time. There are plenty of situations where it might be a little bit more imbalanced and that's totally okay. Alright. So that is validity as it applies to experimental design, and I'll see you guys in the next one. Bye bye.