1.4 Predictions + Descriptive Statistics

Question 1

descriptive statistics

Answer 1

are used to summarise a collection of data

Question 2

mode

Answer 2

• most common score
• advantage: real scores that actually do occur
• dis: depend on how you group the data (e.g. cancers grouped = mode way to die, individually it’s road accidents)

Question 3

median

Answer 3

• middle score (or even = avg of middle 2)
• advantage: not disproportionately affected by outliers, most efficient for estimating population means

Question 4

mean

Answer 4

• balance point of distribution of scores
• advantage: best measure for estimating a population mean from a sample
• dis: usually not real scores, easily affected by extreme values

Question 5

positive skew

Answer 5

mode –> median –> mean
_-‘’—___

Question 6

negative skew

Answer 6

mean –> median –> mode
___—’’‘-_

Question 7

deviation scores will always add up to…

Answer 7

zero

Question 8

TO GET VARIANCE

Answer 8

• figure out deviation scores
• square all the dev scores
• take the sum of all squared deviation scores
• divide by number of scores
• take the square root of this number
  –TA-DAAAA–

Question 9

experimental hypothesis

Answer 9

• prediction of what will happen in your study
• should be derived either from previous findings/literature, or from a theory
• must know which kind of result will prove and which will disprove your theory

Question 10

null hypothesis

Answer 10

• idea that there is no relationship in the population and the relationship in the sample reflects only sampling error
• helps combat only testing/drawing conclusions on what we already believe
• if we reject the null hypothesis, we are saying that we have evidence of an effect not due to chance