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Research 3202c > Week 7 Descriptive & inferential stats > Flashcards

Week 7 Descriptive & inferential stats Flashcards

1
Q

What is statistics?

A

Practice or science of collecting & analysing numerical data in large quantities, especially to make inferences on a population based on a representative sample

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2
Q

Descriptive stats

A

Make descriptions & summaries of population through numbers,graphs: central tendency, data spread, count, proportion, skewness etc

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3
Q

Inferential stats

A

Provide meaningful inferences/conclusion on population based on data collected from a sample, to make generalisations & predictions

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4
Q

What are the types of statistics?

A
  1. Descriptive
  2. Inferential
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5
Q

What are the measures of central tendency?

A

Mean, Median, Mode

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6
Q

When is mean usually used?

A

Suitable for symmetric distribution, often with SD

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7
Q

When is median usually used?

A

Suitable for skewed distribution, often with IQR

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8
Q

Why is median most used in skewed distribution?

A

It is less sensitive to extreme values unlike mean where it is pulled with the direction of skew

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9
Q

What is variance?

A

Average of squared differences of each data point from mean, squared unit of mean

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10
Q

What is standard deviation?

A

Square root of a variance

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11
Q

What does a small & large SD mean?

A

Small - data points are closer around the mean
Large - data points are further to mean

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12
Q

What does small & large variance mean?

A

Small - data are close to mean & each other
Large - data are far from mean & each other

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13
Q

What is the empirical rule?

A

68% - within 1 SD from mean
95% - within 2 SD from mean
99.7% - within 3 SD from mean

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14
Q

What is the purpose of inferential stats?

A

To generalise sample characteristics to population parameters where they are just estimations & have to account for inaccuracies & errors using confidence interval (CI)

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15
Q

What is confidence interval?

A

a range of values where the true mean lies

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16
Q

What is the distribution assumed for parametric tests?

A

Normal (Gaussian) distribution

16
Q

What are the limitations of inferential stats?

A
  1. Can never be fully accurate bc using sample data to estimate that of a population
  2. Interpretation of data is subjected to researchers reasoning
17
Q

What is non parametric test based on?

A

No need to follow normal distribution mostly based on rank order or how common data is

18
Q

What central tendency does parametric & non parametric measure?

A

Para - mean
Non para - median

19
Q

What type of variables does parametric measure?

A

Continuous

20
Q

What type of variables does non parametric measure?

A

Continuous and discrete

21
Q

Assumptions for parametric test?

A
  1. DV is continuous
  2. DV follows normal distribution
  3. Homogenity of variance between groups (same)
  4. Comparison groups are independent
  5. Preferably no significant outliers
22
Q

How to check for normality?

A
  1. Visualisation - Q-Q plot or histogram
  2. Statistical hypothesis testing: Shapiro Wilk test
23
Q

T-test

A

Determine whether there is a significant difference between the means of two groups. It is widely used in hypothesis testing when comparing sample means to make inferences about population mean

24
Q

F-test: ANOVA

A

To compare variances or equality of means among 3 or more groups/conditions

25
Q

What is analysis of variance (ANOVA) for?

A

Analyses how entire set of group means are spread out regardless of group differences

26
Q

What is measures of linear association?

A

Produces a coefficient used to quantify the strength & direction of a rs/association between 2 or more variables where value of coefficient is -1 to +1

27
Q

Examples of non parametric tests

A

Wilcoxon signed rank test
Mann-Whitney U test
Kruskal Wallis test
Friedman’s test

28
Q

Bivariate correlation

A

Focuses on relation between 2 variables, need best fit line for continuous variable - not applicable to curvilinear or discontinuous rs

29
Q

Chi square test of independence

A

Measure significance of association between 2 categorical variables e.g no numerical meaning

30
Q

Linear regression

A

An estimation of association between a continuous DV and more than 1 IV

31
Q

Assumptions of linear regression

A
  1. Linear rs
  2. Independence
  3. Homoscedasticity - variance of scores are similar
  4. Normality
32
Q

What are the types of regression?

A
  1. Simple linear
  2. Multiple linear
  3. Nonlinear
33
Q

Simple linear

A

Compare 2 variables only y=a +bx + c

34
Q

Multiple linear regression

A

Multiple IV and DV - best fit line

35
Q

Logistic regression

A

An estimation of association between a binary DV (yes/no) and more than 1 IV

Research 3202c (9 decks)

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