sampling strategy

Question 1

what are the 5 sampling methods

Answer 1

random
systematic
stratified
cluster
convenience

Question 2

what is the sampling scheme

Answer 2

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

Question 3

name two statistical strategies

Answer 3

frequentist

bayesian

Question 4

name three non-statistical strategies

Answer 4

square root N

management directive

judicial requirements

Question 5

an appropriate sampling strategy is dependent on what

Answer 5

the purpose of the investigation

the customers request

the anticipated use of the results

Question 6

what does a strategy plan provide

Answer 6

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

Question 7

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

Answer 7

the issue of homogeneity

Question 8

one sample is sufficient is the bulk material is what

Answer 8

homogenous

Question 9

how are test results made representative if the bulk material is not homogenous?

Answer 9

several samples from different locations may be necessary

Question 10

where are statistical approaches applicable

Answer 10

when inferences are made about the whole population

Question 11

where are non-statistical approaches applicable

Answer 11

if no inference is to be made about the whole population

Question 12

advantages of sampling plans

Answer 12

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

Question 13

disadvantages of sampling plans

Answer 13

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

Question 14

what is random sampling

Answer 14

In a simple random sample of a given size

All such subsets of the frame are given an equal probability

Question 15

give two examples of how you can make sure your sample is random

Answer 15

Random number table

Random number generator

Question 16

advantages of random sampling

Answer 16

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

Question 17

disadvantages of random sampling

Answer 17

Can be vulnerable to sampling error because of the randomness of the selection

Question 18

population sampling

Answer 18

no of units-->no to sample
1                          1
2-5                     2
6-15                   3
15-25                 4
>25                    5

Question 19

coning and quartering - mixing and sampling

Answer 19

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

Question 20

what is systematic sampling

Answer 20

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

Question 21

what is stratified sampling

Answer 21

Population is divided into subgroups (strata)

Strata are based on specific characteristics

• Appearance
• Age
• Education level
• Etc

Used random sampling with each strata

Question 22

what is cluster sampling

Answer 22

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

Question 23

what is convenience sampling

Answer 23

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

Question 24

what do you have to be wary of in convenience sampling

Answer 24

bias

Question 25

when sampling drugs

Answer 25

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

Question 26

how to get a random sample

Answer 26

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

Question 27

what two principles must be obtained when sampling

Answer 27

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.

Question 28

arbitrary sampling

Answer 28

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

Question 29

example of arbitrary sampling

Answer 29

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

Question 30

frequentist sampling methods

Answer 30

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

Question 31

bayesian sampling methods

Answer 31

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

Question 32

the hypergeometric distribution

Answer 32

• The probability that a sample size n contains X positives (units containing illegal drugs) -equation in notes see notes for examples

Question 33

the binomial distribution

Answer 33

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