sampling strategy Flashcards
what are the 5 sampling methods
random systematic stratified cluster convenience
what is the sampling scheme
- sampling strategy
- population determination
- sampling plan
- sampling procedure
name two statistical strategies
frequentist
bayesian
name three non-statistical strategies
square root N
management directive
judicial requirements
an appropriate sampling strategy is dependent on what
the purpose of the investigation
the customers request
the anticipated use of the results
what does a strategy plan provide
an adequate basis for answering questions of applicable law
If an inference about the whole population is to be drawn from a sample, then the plan shall be statistically based, and limits of the interference shall be documented
what needs to be addressed when a single unit or bulk population is to be analysed?
the issue of homogeneity
one sample is sufficient is the bulk material is what
homogenous
how are test results made representative if the bulk material is not homogenous?
several samples from different locations may be necessary
where are statistical approaches applicable
when inferences are made about the whole population
where are non-statistical approaches applicable
if no inference is to be made about the whole population
advantages of sampling plans
decreases time per case
decreases use of costly chemicals and instrumentation
widely used in the forensic community
usually sufficient to prove possession/supply of a controlled substance
disadvantages of sampling plans
means that some items are not tested
can be confusing to explain
in the legal community, there is a lack of understanding/communication
may be challenged in court
what is random sampling
In a simple random sample of a given size
All such subsets of the frame are given an equal probability
give two examples of how you can make sure your sample is random
Random number table
Random number generator
advantages of random sampling
Minimised bias and simplifies analysis of results
The variance between individual results within the sample is a good indicator of variance in the overall population. Makes it easy to estimate accuracy of results
disadvantages of random sampling
Can be vulnerable to sampling error because of the randomness of the selection
population sampling
no of units-->no to sample 1 1 2-5 2 6-15 3 15-25 4 >25 5
coning and quartering - mixing and sampling
In coning and quartering, the sample is manually mixed in on itself for a period of time and the material if formed into a cone
A particle could roll down the cone in any direction, hence the mixing effect
The top of the cone is flattened, and it is divided into 4 quarters
Opposite quarters are combined to give half the sample, and the process id repeated until a suitable sample size is obtained
what is systematic sampling
Relied on arranging the study population according to some ordering scheme
Then selecting elements at regular intervals through the ordered list
Involves a random start and then proceeds with the selection of every *th element from then onwards
what is stratified sampling
Population is divided into subgroups (strata)
Strata are based on specific characteristics
- Appearance
- Age
- Education level
- Etc
Used random sampling with each strata
what is cluster sampling
Most cost-effective to select respondents in groups
Sampling is often clustered by geography or by time periods
Random sampling used to choose clusters
All data used from selected clusters
what is convenience sampling
Sometimes known as grab, accidental or opportunity sampling
A type of non-probability sampling which involves the sample being drawn from the part of the population which is close to hand
what do you have to be wary of in convenience sampling
bias
when sampling drugs
Each group will be considered as a whole population and will be sampled alone
In some rare cases, although the external characteristics look the same, upon opening the units (sampling), difference in the powder appearance among the units may be seen
how to get a random sample
The theoretical way to select a truly random, unbiased representative sample from a population is to individually number each item in the population
Then use a number generation to choose which item to select
This is not possible in practice, especially for large populations containing many thousands of units
what two principles must be obtained when sampling
The properties of the sample are a true reflection of the properties of the population from which the samples were taken.
Each unit in the population has an equal chance of being selected.
arbitrary sampling
They are often used in practice and work well in many situations
However, they have no statistical foundation and may lead to a very large sample to analyse in case of large seizures
example of arbitrary sampling
E.g. All (n = N)
• Advantages
- 100% certainty about the composition of the population
• Disadvantage
- excessive sample sizes for larger populations
n = 0.05N, n = 0.1N etc • Advantage - simple approach • Disadvantage - excessive sample sizes for larger populations
n = √N, n = 0.5 √N, n = √N/2
• Advantage
- widely accepted approach
• Disadvantage
- the number of samples may be too small when the population is small
- excessive sample sizes for larger populations
n = 1 • Advantage - minimum amount of work • Disadvantage - least amount of information on the characteristics of the seizure
Two methods concern a frequentist approach, while the third method describes a Bayesian approach
frequentist sampling methods
The assumption behind a frequentist approach is that a fixed but unknown proportion of the seizure (population) contains drugs
The proportion of drugs in a sample (= the sampled units) can estimate this seizure population
The proportion of drugs in the sample will however vary over different samples
Therefore, the frequentist methods provide a confidence, (1-α)100%
For instance, 95% if α is selected to be 0.05
That with a given sample proportion of the seizure proportion is at least k 100% (for instance 90% if k is selected to be 0.9)
In another words, one would be correct about a seizure containing at least 90% drugs in 95% out of 100 cases
bayesian sampling methods
The assumption behind a Bayesian approach is that the sample proportion is known and fixed
This proportion is used to calculate probabilities on certain value of the unknown seizure proportion that at that point is still assumed variable
the hypergeometric distribution
• The probability that a sample size n contains X positives (units containing illegal drugs)
-equation in notes
see notes for examples
the binomial distribution
The binomial distribution can be used to calculate a sample size n such that with (1-α)100% confidence can be stated at least a proportion of k 100% is positive
The calculations with the binomial distribution are easier than the ones with the hypergeometric distribution
However binomial distributions are approximations
Sample size estimated with it will be slightly overestimated
- Only in very large seizures (sometimes of several thousand) will the sample sizes calculated from both distributions be equal
