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Flashcards for AS Applied Chapter 1 (Statistics - Data collection)
Population
The whole set of items that are of interest in statistics.
Census
An observation or measurement of every member of a population.
Sample
A selection of observations taken from a subset of the population.
Sampling Units
Individual units of a population.
Sampling Frame
A list formed by naming or numbering the sampling units of a population.
Simple Random Sample
A sample where every sample of size n has an equal chance of being selected.
Systematic Sampling
A method where required elements are chosen at regular intervals from an ordered list.
Stratified Sampling
A sampling method where the population is divided into mutually exclusive strata and a random sample is taken from each.
Quota Sampling
A sampling method where an interviewer selects a sample that reflects the characteristics of the whole population.
Opportunity Sampling
A method of sampling where participants are selected based on their availability and fit for the study criteria.
Quantitative Variables
Variables associated with numerical observations.
Qualitative Variables
Variables associated with non-numerical observations.
Continuous Variable
A variable that can take any value within a given range.
Discrete Variable
A variable that can take only specific values within a given range.
Grouped Frequency Table
A table that presents data in groups or classes without showing specific data values.
Class Boundaries
The maximum and minimum values that belong in each class of a grouped frequency table.
Midpoint
The average of the class boundaries in a grouped frequency table.
Class Width
The difference between the upper and lower class boundaries.
Census (Advantages)
It should give a completely accurate result
Census (Disadvantages)
Time consuming and expensive
Cannot be used when the testing process destroys the item
Hard to process large quantity of data
Sample (Advantages)
Less time consuming and expensive than a census
Fewer people have to respond
Less data to process than in a census
Sample (Disadvantages)
The data may not be as accurate
The sample may not be large enough to give information about small subgroups of the population
Types of Random Sampling (3)
Simple random sampling
Systematic sampling
Stratified sampling
Simple Random sampling (Advantages)
Free of bias
Easy and cheap to implement for small populations and small samples
Each sampling unit has a known and equal chance of selection
Simple Random Sampling (Disadvantages)
Not suitable when the population size or the sample size is large
A sampling frame is needed
Systematic sampling (Advantages)
Simple and quick to use
Suitable for large samples and large populations
Systematic sampling (Disdvantages)
A sampling frame is needed
It can introduce bias if the sampling frame is not random
Stratified sampling (Advantages)
Sample accurately reflects the population structure
Guarantees proportional representation of groups within a population
Stratified sampling (Disadvantages)
Population must be clearly classified into distinct strata
Selection within each stratum suffers from the same disadvantages as simple random sampling
Types of Non-random Sampling (2)
Quota sampling
Opportunity sampling
Quota sampling (Advantages)
Allows a small sample to still be representative of the population
No sampling frame required
Quick, easy and inexpensive
Allows for easy comparison between different groups within a population
Quota sampling (Disadvantages)
Non-random sampling can introduce bias
Population must be divided into groups, which can be costly or inaccurate
Increasing scope of study increases number of groups, which adds time and expense
Non-responses are not recorded as such
Opportunity sampling (Advantages)
Easy to carry out
Inexpensive
Opportunity sampling (Disadvantages)
Unlikely to provide a representative sample
Highly dependent on individual researcher
Note 
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