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Druga of Abuse > Sampling strategy > Flashcards

Sampling strategy Flashcards

1
Q

why is sampling strategy important

A
  • for efficiency

- need to know how to select the appropriate sampling strategy

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2
Q

sampling scheme

A
  1. sampling strategy
  2. population determination
  3. sampling plan
  4. sampling procedure
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3
Q

an appropriate sampling strategy is dependent on what

A
  • The purpose of the investigation
  • The customer’s request
  • The anticipated use of the results
    All this should be taken into account when designing a sampling scheme
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4
Q

what does a sampling plan provide

A
  • Adequate basis for answering questions of applicable law
    e. g. is there a drug present in the population
  • 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 inference shall be documented
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5
Q

statistical sampling strategies

A

frequentist

bayesian

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6
Q

non- statistical sampling strategies

A

square root N
management directive
judicial requirements

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7
Q

what forms the foundation of most sampling strategies

A

laws and legal practice

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8
Q

what two approaches may a sampling strategy take

A

statistical or non statistical approach

In many cases, a non statistical approach may suffice

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9
Q

what does a sampling strategy provide

A
  • Adequate basis for answering questions of applicable law
    e. g. is there a drug present in the population
  • 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 inference shall be documented
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10
Q

what needs to be addressed when a single unit or bulk population needs to be analysed

A

the issue of homogeneity

  • one sample is sufficient if the bulk material is homogeneous
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11
Q

what if the bulk material is not homogenous

A

several samples from different locations may be necessary to ensure that the test results are representative (to avoid false negative results)
- Depending upon the inference to be drawn/ what needs to be known form the analysis for a multiple unit population (may be statistical or non statistical)

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12
Q

statistical approaches

A
  • Applicable when inferences are made about the whole population for example:
    The probability that a given % of the population contains the drug of interest or is positive for a given characteristic
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13
Q

non statistical approaches

A

appropriate if no inference is to be made about the whole population
You just want to know if a drug is present or not

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14
Q

sampling plans advantages

A
  • decrease time per case
  • decrease use of costly chemicals and instrumentation
  • widely used in the forensic community
  • usually sufficient to prove possession/supply go a controlled substance
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15
Q

sampling plans disadvantages

A
  • 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
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16
Q

basic sampling methods

A
  • Random
  • Systematic
  • Stratified
  • Cluster
  • Convenience
17
Q

what is random sampling

A
  • in Simple Random Sample of a given size
    All such subsets are given an equal proability
    Or the pick a name out of the hat technique
18
Q

how to make sure you sample is random in random sampling

A

Random number table

Random number generator

19
Q

advantages of random sampling

A
  • Minimises bias and simplifies analysis of results
  • The variance between individual results within the sample is a good indicator of variance in the overall population
    Making it relatively easy to estimate the accuracy of results
20
Q

disadvantages of random sampling

A
  • SRS can be vulnerable to sampling error because of the randomness of the selection
    May result in a sample that doesn’t reflect the makeup of the population
21
Q

population sampling guidelines

A 
no of units -> no of sample
1-> 1
2-5 -> 2
6-15 -> 3
15-25 -> 4
>25 --> 5
22
Q

mixing and sampling

A
  • In coning and quartering, the sample is manually mixed in on itself for a period of time and the material is 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 is repeated until a suitable sample size is obtained.
23
Q

systematic sampling

A
  • Relies 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 the proceeds with the selection of every kth element form then onwards
  • It is important that the starting point is randomly chosen
24
Q

stratified sampling

A
  • Population is divided into subgroups (strata)
  • Strata are based on specific characteristics
    Age
    Appearance
    Education level
    Etc.
  • Use random sampling within each strata
25
Q

cluster sampling

A
  • more 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
26
Q

convenience sampling

A
  • 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
  • Be wary of bias
27
Q

when sampling drugs

A
  • 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
28
Q

getting a random sample

A
  • 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
29
Q

when sampling we must ensure that two principles are maintained

A
  • 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.
30
Q

what is arbitrary sampling

A
  • 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
    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
  • Disadvantage: 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
31
Q

statistical sampling method

A
  • Two methods concern a frequentist approach, while the third method describes a Bayesian approach
32
Q

frequent sampling methods

A
  • The assumption 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
33
Q

bayesian sampling methods

A
  • 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
34
Q

they hypergeometric distribution

A
  • The probability that a sample size n contains X positives (units containing illegal drugs)
  • Given that the population of size N contains N1 positives, can be calculated by

(equation in powerpoint)

and see notes for examples

35
Q

the binomial distribution

A
  • 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

see notes for examples

Druga of Abuse (11 decks)

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