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Stats refresher > Intro to statistics > Flashcards

Intro to statistics Flashcards

1
Q

What is a null hypothesis?

A

Difference between scores is so small that it is likely caused by chance.

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2
Q

What is an experimental/alternative hypothesis?

A

Any difference in scores between conditions is large enough that it is likely not caused by chance.

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3
Q

What is considered statistical significance?

A

1 occasion in 20, or P<0.05, or 5%.

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4
Q

What is a type 1 error?

A

Result is declared statistically significant where there is not a significant difference.

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5
Q

What is a type 2 error?

A

Result is declared not statistically significant where there is a significant difference.

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6
Q

What is a one-tailed hypothesis?

A

Predicts the direction of outcome.

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7
Q

What is a two-tailed hypothesis?

A

Does not predict the direction of outcome but still predicts statistical significance.

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8
Q

What is nominal data?

A

Data split into different categories with no order or direction e.g male/female, colours of sweets in a pack.

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9
Q

What is ordinal data?

A

Data has ordered categories where differences between them are not known e.g rankings/scales, places in a competition, you cannot assume the difference between 1st and 2nd place is the same as the difference between 3rd and 4th.

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10
Q

What is interval data?

A

Data measured on a numerical scale with equal distances between values e.g time, weight.

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11
Q

What are pairwise comparisons?

A

Comparing things in pairs to see if they are statistically different to each other.

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12
Q

What is effect size?

A

The magnitude of the relationship between variables.

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13
Q

What is the alpha criterion?

A

Probability of making a type 1 error.

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14
Q

What are post-hoc analyses?

A

Done after the data has been collected and analysed. Control for type 1 errors.

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15
Q

What are a-priori analyses?

A

Done before the experiment begins.

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16
Q

What is a chi-square stats test used for?

A

Analysing nominal data to compare the distribution of observations with those expected to be by chance.

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17
Q

When would you use a Yates correction?

A

When performing a chi-square test with 1 degree of freedom. Due to overestimation of the test statistic.

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18
Q

What is a T-test used for?

A

To compare means. Using interval or ratio data.

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19
Q

What is the standard error?

A

The standard amount by which a sample mean is in error when estimating the mean of the population.

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20
Q

What do T scores tell us?

A

In an independent they tell us how different our sample mean is from the population mean. In a related, they tell us the mean difference between two sets of scores.

21
Q

What is a correlation?

A

A measure of the linear relationship between two variables.

22
Q

When would a Pearson’s correlation be used?

A

For interval variables.

23
Q

When would a Spearman’s Rho correlation be used?

A

For ordinal variables.

24
Q

What is a correlation coefficient?

A

The amount of variance shared by the variables.

25
Q

What do ANOVA’s tell us?

A

If there are statistical differences between independent groups. Then you can understand which independent variable(s) has a connection to your dependent variable.

26
Q

What is a one-way ANOVA?

A

Compares the effects of an independent variable on one or multiple dependent variables.

27
Q

What is a two (or more)-way ANOVA?

A

Compares the effects of more than one independent variable on one or multiple dependent variables.

28
Q

What does a ‘factorial’ ANOVA mean?

A

Any ANOVA that has two or more independent categorical variables.

29
Q

Why would ad-hoc tests be needed for an ANOVA?

A

As they do not tell you which groups are different from each other.

30
Q

What are the assumptions of an ANOVA?

A
  1. Interval data
  2. Normality
  3. Homogeneity of variance
  4. Independence
31
Q

What is the meaning of normality in stats?

A

Each ps supplies one piece of data and it is organized according to its numeric quantity, so the mean is accurate.

32
Q

What is the meaning of homogeneity of variance?

A

The dependent variable scores have the same degree of variability across conditions

33
Q

What is the meaning of independence in stats?

A

The absence of systematic bias from nuisance variables across and within conditions.

34
Q

What are the methods of mediating the negative impacts of a repeated-measures experimental design?

A
  1. Counterbalancing
  2. Randomization
35
Q

What is counterbalancing?

A

Half of participants do condition A then B, the other half do B then A.

36
Q

What is randomization?

A

Different order of conditions is generated randomly for each participant.

37
Q

Would using counterbalancing and randomization fix carryover effects?

A

No.

38
Q

What are carryover effects?

A

The changes in participants that can be caused by a condition.

39
Q

What is statistical power?

A

Probability of correctly rejecting a false null hypothesis, and finding an effect if it actually exists.

40
Q

What is the minimum desired statistical power?

A

0.80 or an 80% chance of finding a true effect.

41
Q

What can influence statistical power?

A
  1. Sensitivity of the study
  2. Size of effect of interest
  3. Alpha criterion
42
Q

How can you know what statistical power to expect?

A
  1. Look at previous studies
  2. Run a pilot study
  3. Use Cohen’s ranges
43
Q

How should power be calculated for factorial designs?

A

Required power should be calculated separately for all main effects and interactions.

44
Q

When should non-parametric tests be used?

A

When assumptions for the parametric tests fail.

45
Q

What is kurtosis?

A

The thinness of a distribution.

46
Q

What are the 3 types of kurtosis?

A

leptokurtic = very thin/high to the top of the graph
mesokurtic = middle thinness
platykurtic = very fat/low to the bottom of the graph

47
Q

What is the interquartile range?

A

three values that split the sorted data into 4 parts.

48
Q

What are the quartiles?

A

Second = median
Lower = median of lower half of data
Upper = median of upper half of data

49
Q
A

Stats refresher (4 decks)

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