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Research Methods > W4 - Stats Refresher > Flashcards

W4 - Stats Refresher Flashcards

1
Q

What are the 2 most commonly used parametric tests in experimental work?

A

ANOVA

T-tests

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2
Q

Define variance

A

How spread out data is in relation to the mean + how close ind values are to the avg value.

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3
Q

What is variance expressed as?

A

Sigma squared

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4
Q

What is the variance of a data set describing?

A

Avg error between mean value + ind values of a data set

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5
Q

What is population variance describing?

A

Variance of the entire pop of interest

Usually hypothetical unless we can measure every person in the pop

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6
Q

How is population variance calculated?

A

Total deviance / Sample size

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7
Q

Define sample variance

A

Variance of our experimental sample

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8
Q

How is sample variance calculated?

A

Total deviance / degrees of freedom

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9
Q

Which is smaller than which?

Sample variance or population variance

A

Population variance is always smaller than sample variance.

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10
Q

What can be derived from variance?

A

SD

SEM

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11
Q

What is SD equal to?

A

Square root of the variance

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12
Q

Define SD

A

Variability of data around the mean

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13
Q

What % of the population fall within 1 SD of the mean?

A

~68%

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14
Q

What % of the population fall within 2 SD of the mean?

A

95%

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15
Q

How is SEM or SE calculated?

A

SD / Square root of Sample size

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16
Q

How might you examine variance in a visual manner?

A

By looking at error bars in graphs.

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17
Q

Define deviance

A

How different each value is from the mean

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18
Q

How is deviance calculated?

A

Calculate mean of data set

Each ind value - mean = deviance value

Square each deviance value

Add each squared deviance value to give the SS

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19
Q

Define the SS

A

Total dispersion or deviance of scores from the mean

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20
Q

Disadvantage to deviance

A

Size of deviance is dependent on how many values are in data set

Meaning its difficult to compare the SS from one data set to the SS of another data set with a different number of values.

Instead you would use avg dispersion rather than total dispersion. Avg dispersion is the variance.

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21
Q

SPSS output

What does it mean when the skewness value is more than double the SE of skewness?

A

MAY have a skewed distribution

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22
Q

SPSS output

What does it mean when the kurtosis value is more than double the SE of kurtosis?

A

May have a skewed distribution / non-normal distribution

23
Q

What are the statistical tests of normal distribution?

A

Kolmogorov-Smirnov test

Shapiro-Wilk test (better for sample sizes <50)

24
Q

What do statistical tests of normal distribution do?

A

Compare values in a sample to a normally distributed set of values with the same mean + SD.

25
Q

Statistical tests of normal distribution…

p>0.05

A

Non-significant

Distribution of sample isn’t significant from a normal distribution so the distribution of data set is probably normal.

26
Q

Statistical tests of normal distribution…

p<0.05

A

Significant

Distribution of data is sig different from a normal distribution so the distribution of data set is probably NOT normal.

27
Q

What does it mean if tests indicate data is normally distributed?

A

Data meets the assumption of normality

28
Q

What if the data is not normally distributed?

A

Check + remove for outliers ONLY if you have good reason to believe that person does not belong to the population you want to sample.

Use a non-parametric test (but means there will be a reduction in experimental power)

Easy way to normality –> TEST MORE PEOPLE

29
Q

How can a data set be trimmed?

A

Prod a stem + leaf plot in SPSS to ID any outliers.

Most researchers use 1 or 2 rules to trim the data set.

30
Q

What are the 2 rules most researchers use to trim the data set?

A

A % based rule

SD based rule

31
Q

Trimming the data set

What does the % based rule involve?

A

Deleting a certain % of the values in your data set

i.e highest + lowest 10%

Mean of that is then the trimmed mean.

32
Q

Trimming the data set

What does the SD based rule involve?

A

Calculate mean + SD

Remove values that are a certain number of SD from the mean

33
Q

What is the purpose of t-TESTS?

A

To give a value for the sig of the differences between the groups or conditions

34
Q

Independent samples t test

A

2 individual groups

2 experimental conditions + different participants assigned to each one

35
Q

Assumptions to the independent t test

A

Data is continuous

Data is interval or ratio

Both groups drawn at random from pop = independent

Normally distributed data

Homogeneity of variance between the samples

36
Q

Paired / 1 sample / Dependent t test

A

Comparing 1 group across 2 conditions

37
Q

Assumptions to the paired / 1 sample / dependent t test

A

Data is continuous

Data is interval or ratio

Differences are normally distributed

Homogeneity of variance

Both groups drawn at random from pop = Independent

38
Q

How is the output reported from SPSS for the independent samples t test

A

Each groups mean + SE/SD
Mean difference + CI range
T-value, df + p value

t(df) = t, p = significance value

39
Q

How is the output reported from SPSS for the dependent/paired/1 samples t test

A

Each condition’s mean + SE/SD.
Outline difference + the CI range
T-value, df + p value.

40
Q

What is the t score/ t value

A

Difference between groups : difference within groups

41
Q

What does a larger t-score / t-value mean?

A

More difference between groups or conditions

42
Q

What does a smaller t-score / t-value mean?

A

More similarity between groups or conditions

43
Q

What would a t-value of 3 indicate?

A

Groups/conditions are 3 times as different from each other as they are within each other

44
Q

When running a t-test what does a bigger t value indicate?

A

More likely the results are repeatable.

45
Q

Define homogeneity of variance

A

Both groups have a similar spread of values around the mean

46
Q

Levene’s test for homogeneity of variance

When is the value for the levene’s test NOT significant and what does this mean?

A

Not sig when p>0.05

So –> Accept assumption of homogeneity of variance

Use equal variances assumed from SPSS

47
Q

Levene’s test for homogeneity of variance

When is the value for the levene’s test IS significant and what does this mean?

A

p<0.05

Assumption of homogeneity of variance is violated

Use equal variances not assumed

48
Q

Give an example of a 1-tailed test

A

Do people perform better under drug A than drug B

49
Q

Give an example of a 2-tailed test

A

Is there a difference in how drug A and drug B affect performance

50
Q

In theory, which tailed test has how much more statistical power to detect an effect than the other?

A

In theory, 1-tailed tests have twice as much statistical power to detect an effect.

51
Q

Which tailed test has more chance of finding a significant difference between groups or samples?

A

1-tailed tests

52
Q

Why do 1-tailed tests have more chance of finding a significant different between groups or samples than 2-tailed?

A

Because you need less participants to reach significance.

53
Q

In practise are 1-tailed tests used commonly?

A

No, rarely.

Research Methods (13 decks)

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