DATA ANALYSIS – REVISION AND KEYWORDS
POPULATION – represents the complete set of items that interest an investigator.
Population is ever changing – thus a dynamic concept
Population sample size – represented by N
SAMPLE – part or a fraction of the population
Sample size – represented by n
Getting access to entire population is prohibitively costly – sampling is used
TYPES OF SAMPLING:
SIMPLE RANDOM SAMPLING
Each member of the population chosen strictly by chance
Each member of the population equally likely to be chosen
Selection of one member does not influence selection of any other
members
Every possible sample of n objects is equally likely to be chosen
SYSTEMATIC SAMPLING
A specific order – e.g. randomly selecting every 10 names from a long list
STRATIFIED SAMPLING
CLUSTER SAMPLING
MULTI-STAGE SAMPLING
TWO BRANCHES OF STATISTICS:
DESCRIPTIVE STATISTICS – using the sample data to describe and draw
conclusions about the sample only
Graphical and numerical procedures to summarise and process data
E.g. surveys (collect), tables and graphs (present), sample mean
(summarize)
INFERENTIAL STATISTICS – using the sample data to draw conclusions about
the population
Using data to make predictions, forecasts and estimates to assist decision
making
- Inference – the process of drawing conclusions or making decisions about
a population based on sample results
PARAMETER – a numerical measure (can be quantified) that describes a specific
characteristic of a population
POPULATION – represents the complete set of items that interest an investigator.
Population is ever changing – thus a dynamic concept
Population sample size – represented by N
SAMPLE – part or a fraction of the population
Sample size – represented by n
Getting access to entire population is prohibitively costly – sampling is used
TYPES OF SAMPLING:
SIMPLE RANDOM SAMPLING
Each member of the population chosen strictly by chance
Each member of the population equally likely to be chosen
Selection of one member does not influence selection of any other
members
Every possible sample of n objects is equally likely to be chosen
SYSTEMATIC SAMPLING
A specific order – e.g. randomly selecting every 10 names from a long list
STRATIFIED SAMPLING
CLUSTER SAMPLING
MULTI-STAGE SAMPLING
TWO BRANCHES OF STATISTICS:
DESCRIPTIVE STATISTICS – using the sample data to describe and draw
conclusions about the sample only
Graphical and numerical procedures to summarise and process data
E.g. surveys (collect), tables and graphs (present), sample mean
(summarize)
INFERENTIAL STATISTICS – using the sample data to draw conclusions about
the population
Using data to make predictions, forecasts and estimates to assist decision
making
- Inference – the process of drawing conclusions or making decisions about
a population based on sample results
PARAMETER – a numerical measure (can be quantified) that describes a specific
characteristic of a population
