Cogntive Testing and Statistics

Question 1

What is cognition?

Answer 1

The mental action or process of acquiring knowledge and understanding through thought, experience, and the senses

Question 2

What are we interested in in scientific investigation of cognition?

Answer 2

The mechanisms of thought that drive a person’s mind

Can be impaired - cogntive deficits are distressing to patients

Cognition is driven by multiple processes and functions that work together to create a whole- highly complex

Question 3

What is a caveat of measuring cognition?

Answer 3

Any measurement attempt we make is always a best guess- always caveats and a level of noise that come between us and the thing we want to measure
-affects ability to accurately quantify what we’re interested in

Question 4

Why is measuring cognition hard?

Answer 4

Because the mind itself is a psychological theory - so we have to consider what a psychological theory is and also how accurate it is

We break down the mind into what we think its made up of but we are always one step away from measuring it- we’re dealing with a theoretical construct within a theoretical construct

Question 5

What are cognitive tests if the mind is a psychological theory?

Answer 5

Cognitive tests are attempts at creating markers of something that only exists in a theory

Question 6

What is a normal distribution?

Answer 6

A normal distribution is where there is a clear trend for the value of most points to cluster around a central mean, with equally increasing rarity on either side of this

Many things don’t follow this pattern but it’s used as a starting point to decide how to conduct statistics

Question 7

Why is a normal distribution important?

Answer 7

The central issue is asking if the mean value is a fair way of representing what is “average”

-if we have a normal distribution then the mean of that is the most appropriate value to represent that data

Question 8

What does it mean if something is parametric?

Answer 8

That is has a normal distribution

Question 9

When does data violate parametric assumptions?

Answer 9

The moment it doesn’t follow a mathematically perfect normal distribution (when most data isnt perfect) it means it violates these assumptions

Question 10

What happens when data distribution becomes more skewed?

Answer 10

Once data is skewed, the mean is increasingly pulled in one direction by extreme values.

The median and the mode become more representative of what is most expected.

Question 11

When we have a normal distribution we want to know how variable it is, how do we work this out?

Answer 11

By calculating its variance - so how far away from the mean it is

Question 12

What is variance?

Answer 12

The variance of a sample is the average difference of all individual data points from the mean. It quantifies how tightly clustered (or not) the data is around the mean

It is a slight overestimation, of the sum of squares, of the difference between each individual data point and the mean

Question 13

What is standard deviation (SD)?

Answer 13

Because variance is a version of our data after we have squared everything, its value is greatly increased compared to our original units of measurement

The SD is just the square root of the variance, so it re-converts it back into proportion with our data.

Question 14

What is the relationship between the mean and SD in a normal distribution?

Answer 14

SD has a consistent mathematical relationship with a normal distribution

Question 15

What does it mean if we travel x SD’s away from the mean?

Answer 15

As we travel x SD’s away from the mean, the % of data points included in our range increases by set amounts.

e.g. 1 SD either side of the mean captures the results of about 68% of people

Question 16

When do scientists attribute significance to something?

Answer 16

When it has less than 5% probability of happening by chance

This is often a single data point more than 2 SD’s from the mean (where 2 SDs covers 95% of people)

Question 17

What are the number of points in the data (the sample size) referred to as?

Answer 17

N

Question 18

What does a larger sample mean?

Answer 18

Means we have more confiedence that our means etc are representative of that variables’ true pattern

Question 19

What is the p value?

Answer 19

The objective of stats tests is to summarise your experiment with a p value.
This is a number between 0 and 1, which is essentially the % probability (when multiplied by 100) that the data you have observed has happened by chance.

We are trying to work out if the data are so different that it can’t be caused by noise & there is a reason people are scoring differently

Question 20

What does it mean if p<0.05 ?

Answer 20

That there is a less than 5% probability of it happening by chance alone, that it is “statistically significant”

We would reject the null at this point

Question 21

What are two common statistical designs?

Answer 21

1. Comparing a value between groups of participants , or before/after and intervention: groupwise tests
2. Seeing how one value changes depending on another value: correlations

Question 22

What is parametric groupwise testing?

Answer 22

Comparing a score between two groups

Question 23

What statistical test do we use for parametric groupwise testing that looks at two groups e.g. healthy vs disease groups ?

Answer 23

Independent samples T-test

Question 24

What makes the t value bigger?

Answer 24

Greater distance between means
Smaller SDs
Largers Ns

Question 25

How to we go from a T value to a p value?

Answer 25

Statisticians know the thresholds for t values at different probabilities.

The last thing we need to convert a t statistic to a p value is the degrees of freedom, which is essentially our N but in a t-test we subtract 1 from it

Question 26

How does a higher N affect t-tests?

Answer 26

Having a higher N makes the significance threshold on a t value “easier” to achieve.

Question 27

What statistical test do we use for parametric groupwise testing that looks at a before and after score?

Answer 27

Paired samples t-test

Question 28

What are parametric correlations?

Answer 28

Seeing if there is a relationship between two variables

Question 29

What statistical test do we use for a parametric correlation?

Answer 29

Pearson’s correlation

Question 30

What is a pearson’s correlation?

Answer 30

It calculates the covariance between your X variable and your Y variable

r value comes out between -1 and 1 where:

-1 = perfect negative correlation
0 = no relationship at all
1 = perfect positive correlation

Question 31

What are the three types of correlation?

Answer 31

Perfect positive
Zero
Perfect negative

Question 32

What do we do with skew?

Answer 32

We use non-parametric equivalent statistical tests

Because our parametric tests rely on mean values a skewed distribution becomes a problem for their validity.

We can still run the same experiments (groupwise or correlations) but we must substitute them with non-parametric test alternatives.

Question 33

What does in independent t-test become in non-parametric testing?

Answer 33

Mann-Whitney U

Question 34

What does a paired t-test become in non-parametric testing?

Answer 34

Wilcoxon signed-rank

Question 35

What does a Pearson correlation become in non-parametric testing?

Answer 35

Spearman correlation

Question 36

What is the main difference between parametric and non-parametric tests?

Answer 36

Non-parametric tests often just used a “ranked” version of the data

This is still representative of which samples are bigger than others, but massively reduces the disproportionate effect that extreme values have on the mean.

Question 37

How do we examine multivariate models?

Answer 37

Often we want to look at how multiple variables affect an outcome, all at once.

It is possible (advisable!) to build a single multivariate model to test this.

This is called a multiple regression

Question 38

What does the sig. column show in multivariate models?

Answer 38

Shows the individual p values for each variable

Question 39

What are multiple regressions?

Answer 39

Multiple regressions are not the same as separately running single variable analyses and putting the results in a list. They consider how these variables relate to one another as well in determining the model significance.

Allows us to control for factors we want to exclude

Question 40

What is the multiple comparisons problem?

Answer 40

The more tests we run in a study, the more likely we are to get a “significant” result by random chance

We need to be conscious of this in our study design, and in the way we approach our analyses

Question 41

How can we correct for multiple comparisons?

Answer 41

Bonferoni correction

Question 42

How do you calculate a Bonferoni correction?

Answer 42

Divide 0.05 by the number of comparisons we have run

This is our new significance threshold