Theoretically, random assignment should eliminate (A)...

Theoretically, random assignment should eliminate
(A) sampling error.
(B) the need to use statistics.
(C) concerns over validity.
(D) many confounding variables.
(E) the need for a representative sample.

Key Concepts

Random Assignment

Random assignment is a methodological process in experimental design where participants are allocated to different groups solely by chance. This technique ensures that any preexisting differences among participants are likely evenly distributed across all groups, thereby helping to isolate the effect of the independent variable on the dependent variable.

Confounding Variables

Confounding variables are extraneous factors that can influence the results of an experiment by providing alternative explanations for the observed outcomes. By using random assignment, researchers can minimize the impact of these variables, ensuring that their effects are equally distributed across groups and do not bias the results.

Transcript

00:01 My name is yazid hamami and i'm a psychology tutor.

00:02 Today we're looking at a question that starts off with theoretically.

00:07 Random assignment, which i'm going to label as ra, should eliminate.

00:16 And then we have a few different choices.

00:19 So let's go through those.

00:21 First of all, a sampling error, which i'm going to label as se.

00:26 Sampling error is basically the difference between your sample and the population for whatever measure you're looking at.

00:32 And random assignment is not really going to get rid of sampling error entirely.

00:38 Once again, they do say theoretically, but even in a theoretical sense, sampling error is always a risk when you're not looking at your overall population, and you're just looking at a sample.

00:47 So probably not a, we can cross that out.

00:50 B, they need to use statistics, which i'm just going to call stats.

00:58 You're always going to need to use statistics in analyzing your data.

01:03 This doesn't have anything to do with how you get your population.

01:08 So, for example, even if we're looking at the simplest statistics such as mean, standard deviation, you know, median, stuff like that, we're going to have to use statistics no matter what and that's not affected by our population method.

01:23 So it's not b, ra, i mean, random assignment doesn't eliminate the need to use statistics.

01:29 Then we have c, validity concerns.

01:32 I'm just going to label as validity for now.

01:37 Validity is basically the extent to whatever you say measures something, actually measure something.

01:45 Let me be more clear on that.

01:47 Basically, if i'm trying to design a survey that measures how often people eat pizza, a good valid survey would be seeing how often somebody visits their local pizza shop.

01:59 That's what validity measures...

Please give Ace some feedback