Statistics Flashcards

(38 cards)

1
Q

Contextual info

Demo slide

[Marks/Points required

Topic/Week

A
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2
Q

What is Cohen’s d

Statistics 6

A

– A measure of distance between two condition means which takes variability into account

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3
Q

How do you assess difference between 2 conditons.

Statistics 6

A
  • Calculate and compare descriptive statistics
    – Means, medians, s.d.’s, confidence intervals
  • Calculate “effect size” using Cohen’s d
  • Use some kind of inferential test based on known probability distributions
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4
Q

What is the range of Cohens d and what can be infered from the value

Statistics 6

A

0 - 1
* 0.2 = small effect size (around 85% overlap)
* 0.5 = medium effect size (around 67% overlap)
* 0.8 = large effect size (around 53%)

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5
Q

What are we trying to do when we are hypothesis testin for 2 population means

Statistics 6

A

figuring out if they have significantly different means

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6
Q

How do you calculate sample mean difference

Statistics 6

A

D = Ma - Mb
Difference = Sample mean A - sample mean B

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7
Q

What would the result be for sample mean difference Assuming the null is true.

statistics 6

A

The difference between the two means is 0.

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8
Q

There is a population of left handed people and right handed people.

In a task involving throwing darts, what would be a hypothesis that favours left handed people in an independent t-test testing the difference between means in 2 conditions.

S6

A

Left-handed people will be more accurate in a task involving left-handed dart throwing at a target than right handed people doing the same task

S6

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9
Q

What are the two types of 2 sample T tests and when are they used

s6

A

– Related (or paired or repeated measures) t-test * Use when participants take part in both conditions WITHIN PARTICIPANTS DESIGN
– Independent t-test * Use when participants perform in only one of the two conditions BETWEEN PARTICIPANTS DESIGN

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10
Q

What is the trick for converting Two paired samples means into one sample

S6

A

Taking the difference between the paired data

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11
Q

Imagine you have reason to believe that attainment in some school district varies w/ left/right handedness and decide to test this idea.

Data for left:
Mean = 24.00
S.d = 12.20
n = 30

Data for right:
mean = 16.5
s.d = 11.8
n = 30

Decided on hypotheses youre testing against, Work out whether you are using the T or Z score and, and reach a conclusion on your decided hypotheses.

4 mark qustion , do the question

S6

A
  • Unpaired T-test
  • root (esea^2 + eseb^2)
  • T = 2.42
  • as N = 30 for both samples and 2 sample test –> 2N -2 = 58
  • 2.42 > 2.392 therefore significant evidence for difference between levels of attainment between lefts & rights

You need to use the table Obvs

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12
Q

What is Correlation

Statistics 7 - Correlation & Pearsons R

A

relationship between 2 variables

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13
Q

Are correlated variables independent or non-independent

S7

A

Non-independent

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14
Q

How can causality be inferred from correlation?

S7

A

Trick question - you cant infer causality from correlation

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15
Q

What is covariance

S7

A

A measure of how much two variables vary together

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16
Q

What do High covariance and low covariance indicate?

S7

A

High covariance = scores for one variable change, other variable will also change in a predictable manner
Low covariance = changes in one variable are not accompanied by a predictable change in the other variable

17
Q

What does positive covariance indicate?

S7

A

higher than average values of one variable tend to be paired with higher than average values of the other variable

18
Q

What does negative covariance indicate?

S7

A

that higher than average values of one variable tend to be paired with lower than average values of the other variable.

19
Q

What does zero covariance indicate?

S7

A

if the two random variables are independent, the covariance will be zero
- does not necessarily mean variables are independent, could just be indicative of a non-linear relationship

20
Q

What is pearsons R

S7

A

Shared variance/ total variance

21
Q

what do the scores of pearsons r indicate

A

0 < r < 1 = imperfect positive correlation
-1 < r < 0 = imperfect negative correlation
R ~ 0 = low correlation

22
Q

What are the steps of using Null Hypothesis significance testing for pearsons R

S7

A

1- Formulate hypothesis
Null - no correlation
Research 1 - (1 tailed) there is a positive correlation
Research 2 (1 tailed) there is a negative correlation
Research 3 (2 tailed) there is a correlation (does not commit to a direction)
2 - get data
3 calculate P value
4 - reject null

23
Q

What does the P value of pearsons R tell you?

S7

A

the probability that the correlation coefficient could arise by chance assuming the null is true

24
Q

What are some of the limitations of an extreme Pearsons R?

S7

A

not tell you there is necessarily a correlation between your variables, it could be really really really unlikely but still occur

25
What is the difference between a parametrci and non-parametric tests ## Footnote Statistics 8 - Non parametric hypothesis tests
- **Parametric tests make certain important assumptions** about populations from which data are sampled - **Non-parametric tests make far fewer assumptions** about populations from which data are sampled
26
What are some of the common testing assumptions of parametric testing. ## Footnote S8
– **Populations** from which samples are drawn should be **normally distributed** – **Variances** (s.ds) of the populations should be **approximately equal** – **No extreme scores** (since these have a big impact on the estimated sample statistics)
27
What are the benefits of using parametric testing if it features so many assumptions ## Footnote S8
- Typically more powerful than other other approaches -
28
Why use non-parametric testsing if its less powerful ## Footnote S8
– Because sometimes the assumptions of parametric testing are violated – In this case we can almost always at least try a non- parametric alternative
29
What are some exmaples of non -parametric tests ## Footnote S8
- mann- whitney U test - Wilcoxon signed rank test - Spearmans Rho - 2x2 chi square test - 1 variable chi square test
30
What is the Mann-Whitney U test ## Footnote S8
a Non-parametric alternative for the independent t-test
31
How would one conduct a Mann-whitney U test ## Footnote S8
- create a research hypothesis have an independent groups design - calculate the smallest possible sum of ranks, - calculate the actual sum of ranks - subtract the smallest possible sum of ranks from the actual sum of ranks, - the Mann -w hitney U is the smaller of the two (if you have to variables) - compare P value to 0.05, accept/reject null, conclusive statement
32
What is the wilcoxon signed rank test ## Footnote S8
a Non-parametric alternative for the paired/related test
33
What is Spearmans Rho ## Footnote S8
A non-parametric alternative to Pearsons R Used when the assumptions required for pearson’s r have not been met
34
What would you have to do in order to conduct a spearmans rho test ## Footnote s8
- Convert scores to ranks - Calculate difference in ranks - Square the difference - Calculate spearman's rho using formula
35
If the ranks are inversly related, what value of Spearmans rho would you get ## Footnote S8
-1
36
If the ranks are perfectly related what value of pearsons R would you get ## Footnote S8
1
37
What are some limitations of Spearmans Rho ## Footnote S8
The given formula fails whenever we have tied ranks
38
What is a 1-variable chi square test used for ## Footnote S8
Can be used to **assess whether observed frequencies in categories are different from what might be expected** Might expect equal distribution or pattern in frequencies