ANOVA

Question 1

A Normal distribution is?

Answer 1

Symmetric

Bell shape curve

Question 2

The most probable values on a normal distribution are around?

Answer 2

Around the average
(top of bell curve)

Question 3

The increasingly less probable values on a normal distribution are?

Answer 3

Further away from that average (bottom of bell curve)

Question 4

The parameters for the normal distribution are?

Answer 4

Constant values that determine the shape and position of the distribution

Mean, μ (Greek letter mu) – i.e., the average: determines where the peak of the distribution is located.

Standard deviation, σ (Greek letter sigma) – i.e., the variability: determines the spread of the distribution.

Question 5

The H0 (Null) is true when the 2 population distributions on a graph are?

Answer 5

Mostly overlapping

Question 6

In order to decide to accept or reject the hypothesis, what do we have to take into consideration?

Answer 6

The means μ1 and μ2
of the 2 populations

Is their difference due to chance?

Question 7

Chance refers to all uncontrolled sources of variability in an experiment. Generally, this variability is called what?

Answer 7

Experimental error

Question 8

EXPERIMENTAL ERROR?
Further divided into 2 main types of error:

Answer 8

Measurement error
Individual differences error

Question 9

MEASUREMENT ERROR

Answer 9

Methods used to measure variable is wrong/ biased/ flawed

This creates a systematic error and you may find an effect that was not due to the DV

(Eg- using 2 different methods to measure control and IV groups cognitive ability)

Question 10

When subjects have naturally occurring differences that are hard to control, these differences will lead to deviation of sample distributions from the target population

even under identical treatments and with Zero measurement error (ZME), what is this known as?

Answer 10

INDIVIDUAL DIFFERENCES ERROR

Question 11

Which error is typically more extreme when small sample sizes (denoted N) are used?

Answer 11

Individual Differences Error

Question 12

What is the equation for the Total Deviation?

Answer 12

Between groups
deviation
÷
Within groups
deviation
= Total deviation

Question 13

If the between groups deviation is large what does that suggest about Exam results and Lecture teaching styles?

Answer 13

The exam scores are highly affected by the lecture teaching styles

Question 14

Between groups variance helps us to explain?

Answer 14

Estimates the treatment effects
The experimental error

Question 15

Within groups variance helps us to explain?

Answer 15

Estimates experimental error ONLY

Question 16

Each individual has an exam score and that score is subtracted from the mean average across both of lecture teaching style conditions.

What is this statement referring to?

Answer 16

Total Deviation

Question 17

What is the name of the ratio ANOVA uses?

Answer 17

F-ratio
Between and Within
both to test the H0

Question 18

What is the equation for Variance?

Answer 18

Variance=
Sum of Squares
÷ Degrees of freedom

Question 19

The Sum of Squares SS

(Within Groups)

Answer 19

Take the Within Groups deviation

square it

Then times that accross every subject in both groups

Question 20

The Sum of Squares SS

(Between Groups)

Answer 20

Take the Between Groups deviation

square it

Then times that across every subject in both groups

Question 21

What does SS Total stand for?

Answer 21

Sum of Squares Total

Question 22

The Sum of Squares Total can be calculated by?

Answer 22

Take the Total deviation (Difference between every student exam score - grand mean of both groups)

square it

Then times that by every individual subject and the number of groups

Question 23

What does this represent:

SS 𝑇= SS𝑅 + SS𝐴

Answer 23

Sum of Squares Total =

Sum of squares Withing Groups
+
Sum of Squares Between Groups

Question 24

The normal distribution can be described by which 2 parameters?

Answer 24

mean μ
and standard deviation σ

Question 25

Our goal in analysis of variance (ANOVA) is to determine…

Answer 25

whether an observed difference in sample means can be generalised to population means,

given our uncertainty about these

Question 26

Why do smaller sample sizes result in more uncertainty?

Answer 26

Because parameters vary more across samples