Introduction to statistics

Question 1

What are the two types of statistics?

Answer 1

• Descriptive Statistics
• Inferential Statistics

Question 2

What is the purpose of descriptive statistics?

Answer 2

• Describe data
• Summarize data
  For example
• How many people got each score
• The standing of a score relative to other scores
• Graphically summarizing set of scores

Question 3

What are the types of descriptive statistics?

Answer 3

• Frequency distribution
• Central tendency
• Variability

Question 4

Frequency Distribution

Answer 4

• Number of participants in total or each category
• A full glance without overwhelmed by raw scores
• Visual assessment
• The sum of frequency should be equal to n
• Possible score and frequency of occurrence

Question 5

What are the characteristics of distribution shapes?

Answer 5

• Modality
  Number of humps in a distribution
• Skewness
  Symmetrical or not (leaning on to one side over the other?)
• Kurtosis
  The relative peakedness or flatness of a distribution compared to normal distribution

Question 6

What is a normal distribution?

Answer 6

• Bell-shaped curve
• The majority of the scores in the centre
• Skewness and kurtosis less than +/- 1(strict)

Question 7

What are statistical assessment one can do to check normal distribution?

Answer 7

• Kolmogorov-Smirnov Test
  If n is larger than 50
• Shapiro-Wilk test
  N smaller than 50
• The tests should not be significant, if they are the groups are too different from known populations

Question 8

What happens if there is no normal distribution?

Answer 8

• Mann Whitney test (independent groups)
• Wilcoxson test (paired groups)
• Non-parametric data, instead of t-tests

Question 9

What are different frequency shapes that is not normally distributed?

Answer 9

• Positive skew
  On the right side, tail pointing toward the higher score
• Negative score
  On the left side, tail pointing toward lower score
• Leptokurtic
  Symmetrical in shape but central peak is higher; more frequent scores near the mean, thus less variability
• Platykurtic
  Symmetrical, the frequency of most values are the same so a flatter curve

Question 10

What can happen when data is not normally distributed?

Answer 10

• Positively skewed; inflated mean
• Negatively skewed; deflated mean
• Leptokurtic; off little variation in the data, so too little differences between people
• Platykurtic; too much variation
• Less confidence in the outcome of parametric tests

Question 11

Central Tendency

Answer 11

• Describe the average score on a variable
• Ideally a singly value

Question 12

What are the three common measures of central tendency?

Answer 12

• Mean
  The average score in the distribution
• Median
  The middle score
• Mode
  Most frequent occurring score in the distribution

Question 13

Variability

Answer 13

• The differences between the samples are with respect to variability
• How spread out are the scores in a distribution

Question 14

What are different measures of variability?

Answer 14

• Range
• Interquartile Range
• Standard Deviation

Question 15

What is standard deviation?

Answer 15

• Conceptually it is an average deviation score
• How big one step is from the mean

Question 16

What rule applies if data is normally distributed?

Answer 16

• 68, 95 and 99.7 % are 3 steps away from the mean

Question 17

Inferential Statistics

Answer 17

Analyses one conduct in order to draw conclusion from their data and to be able to test hypothesis

Question 18

Indirect Approach - Hypothesis Testing

Answer 18

• Obtain sample from population
• Compute statistics
• Infer relations in population from the sample

Question 19

Null Hypotheses

Answer 19

That there is no effect
Example; KBT has no effect on depression
- Often what gets tested
- Less than 5% means that the likelihood of getting our finding by change is less than 5%
- 95% confidence its not random

Question 20

Alternative Hypotheses

Answer 20

That there is an effect

Question 21

Type 1 Error

Answer 21

• False positive
• Detect a significant result
• 2-tailed tests often eliminate these errors
• Set significant to .01
• Too many comparisons

Question 22

Type 2 Error

Answer 22

• False negative
• Saying there is no effect despite being one
• Saying there is no effect despite there being one
• Increase sample size
• .80 Power

Question 23

Effect sizes

Answer 23

The actual magnitude of the difference between groups or the magnitude of the association between variables
- Might be statistically significant but is the effect size big enough?
- Strenght of the relationship; size of association or group means

Question 24

What are the 4 general assumptions of parametric tests?

Answer 24

• Dependent variable is normally distributed
• Homogeneity of variance
• Outcome variable is continuous
• Independence of observations

Question 25

Assumption - Dependent variable is normally distributed

Answer 25

• Problematic with smaller sample sizes
• Check skewness, kurtosis, histogram and conducting tests

Question 26

Assumption - Homogeneity of variance

Answer 26

Variances of the dependent variable are equal across different groups or conditions in statistical tests like ANOVA.
- Important in t-test and ANOVA
- Should not be statistically significant
- Levene’s test
- Falsey rejecting null if violated

Question 27

Assumption - Independence of observations

Answer 27

Data obtained from participants is independent
- No observation bias

Question 28

Assumption - Outcome variable is continious

Answer 28

• Should be measured on an interval or a ratio scale
  Likert scale
  “1.Disagree - 5.Agree”

Question 29

What is standard error and its relation with sample size?

Answer 29

• Calculated error between my sample and the whole population
• The higher sample size, the smaller the error i.e closer to real population