Week 7: Descriptive Statatistics Part 1 and 2

Question 1

What is the difference between a population and a sample

Answer 1

Population: is the entire group of cases about which conclusions will be drawn. Often infinite in size. The properties of a population are called parameters
Samples: subset of the population, finite number, able to be measured. Properties from the sample are called statistics

Question 2

What is statistics?

What is the purpose of statistics?

Answer 2

Data that is obtained by measuring a characteristic on a sample of subjects. The variable is the characteristic being measured that varies among subjects.
Purpose? Describes and summarises info
-makes predictions or generalisations about populations based on results of the sample

Question 3

What are the two types of statistics?

Answer 3

1. Descriptive statistics: describes and summarises information
2. Inferential statistics: provides predictions about populations characteristics based on information taken from samples

Question 4

What are the four variable measurement scales? Give an example for each

Answer 4

1. Nominal- classifying subjects into categories eg gender, ethnicity, religion
2. Ordinal- categories are ordered eg socioeconomic status (low, middle, high)
3. Interval- more like numbers. Uniform intervals between consecutive measures
   - has no true zero point eg blood pressure, temperature
4. Ratio- uniform intervals between consecutive measures
   - has a true zero point eg yearly income, age etc

Question 5

What is the measurement scale for the following?
Working status?
Lumbar flexion?
Patient satisfaction (very satisfied, somewhat satisfied, somewhat dissatisfied, very dissatisfied)

Answer 5

G

Question 6

Discrete vs continuous variable

Answer 6

Discrete variable: separate individual, invisible categories

• no values exist neighbouring categories
• typically whole counting numbers
• always nominal level, and sometimes ordinal level data

Continuous variables: a variable can be divided into infinite number of fractional parts. Eg time, distances weight
-always interval and ratio, and sometimes ordinal level refer to slide 15

Question 7

Frequency distribution. Why are these important?

Answer 7

• it’s a tally of the number of times an individual score is represented in a sample drawn from a population
• important for determining the appropriate statistical test

Question 8

Describing a frequency distribution: what are the 4 basic characteristics of a distribution (descriptive statistics): explain them

Answer 8

1. Central tendency: the two most common measures of location is mean and median
2. Variability: a function of consistency in the data. The two ways to recognise variability
   A) variance: the variance is calculated as the sum of the squared differences between individual scores of the population. In units square
   B) standard deviation: the square root of the variance. It is a indicator of the average deviation of scores about the mean
   Variance and SD tell us how tightly the data congregates about the mean. The greater the SD, the wider the dispersion
3. Skewness: measures of central tendency
   -normal curve= symmetrical
   -positive/negative skew
   -skewness coefficient (mean-median)/SD
   -negative skew: mean < median
   -positive skew: mean > median

Question 9

Displaying data: histogram and bar charts, what the difference?

Answer 9

Histogram: no spaces between bars

Bar charts: spaces between