Okay, so let's finish up talking about experiments. Now in order to make our experimental design as strong as possible, ideally it should be double blind or at the very least single blind. So double blind refers to situations where both the participant and the researcher who is in contact with them are unaware of what condition the participant is in. So if both the experimenter and participant are blind to the condition then that is called a double blind study. Now that is not always possible and so we can also have a single blind study which is when either the participant or the researcher is unaware of the condition.
So, for example, if we had a situation where the experimenter knew the condition but the participant was blind to it, that would be called a single blind study. Now the reason that we like to add this layer of blindness whenever possible is because it can reduce both experimenter effects and demand characteristics. And these terms refer to situations where basically people are altering their behavior in some way because they think they know what condition that participant has been placed in. So if an experimenter does this it's called an experimenter effect and if the participant does it it's called a demand characteristic, but these terms refer to a very similar phenomenon where basically just knowing the condition makes the person behave in a different way even if it's more just like kind of subconscious. It just kind of alters their behavior and we don't want that.
We want the independent variable to be driving the participant's behavior, not anything else, right? So that is blindness. Now one last very important thing to know about experiments is that experiments can be used to establish a cause and effect relationship among our variables and they are the only method in psychology that can do this. Okay? And there are two main reasons why experiments can do this.
So the first is that experiments establish something called temporal precedence which sounds very fancy but basically temporal just means time and precedence is basically saying, like, what comes first in time? So in experiments, the manipulation of the independent variable always comes before the measurement of the dependent variable. So basic logic just dictates that we can confidently say it is possible for this variable to cause this variable. You don't always get temporal precedence with like observational studies or survey research or case studies but you do always get it with experiments. Now experiments also isolate the independent variable by eliminating confounding variables more so than any other psychological method.
So by doing things like having random assignment, making your groups equivalent, by adding blindness, having these controlled laboratory settings, all of those things eliminate confounding variables and isolate the IV so we can confidently say only the independent variable could be causing changes in the dependent variable. Again, other methods in psychology just do not do that as well as experiments. So in terms of the strength of experiments, like we just talked about, that level of control that you're going to get with an experiment is basically unmatched among any other psychological method. So they have a great level of control, and again, they are the only psychological method that truly allows us to establish causality among our variables. K.
So with an experiment, you can say the independent variable caused the dependent variable to happen. With other types of methods, observational research, you know, survey research, you can establish a relationship. You can say this IV is related to this DV, but you cannot say that it caused that dependent variable. So that is the difference there. Now in terms of the limitations of experiments, the situation that you have your participants in can feel a little bit artificial sometimes.
And because of that, sometimes the results don't necessarily generalize to real world behavior as much as you might want them to. So, you know, for example, if you were studying competitiveness and you have people coming into your lab and doing, like, a little click and point computer game, you know, well, the amount of competitive behavior that they display in your lab probably isn't the same as the amount that they would display when they're, like, playing against their rival soccer team or when they're in the middle of a tennis game or whatever. Right? Though because that situation is kind of artificial, the result might not generalize. This is not always an issue but it can be so just kind of keep an eye out for it.
And I also have a note here that poor experimental design can compromise data. So it is true that experiments in general can establish causality. However, if an experiment is not well designed, then it cannot establish causality. So if we don't have random assignment, if there is no level of blindness, if we're not eliminating confounding variables, then it would lose the ability to really establish that cause and effect relationship. So when you are evaluating experiments, be critical of them.
They need, you know, they need harsh criticism because if they're really going to make a causal claim, we have to make sure that they have really good design. Alright. So those are experiments, and I'll see you guys in our next one. Bye bye.