Random Sampling
- Finding out about the abundance and distribution of populations can be achieved by counting all of the organisms present in a habitat
- This is possible for areas that are very small or where the species involved is very large
- For larger and more complex habitats it is not possible to find, identify, and count every organism that is present
- When this is the case, sampling can be used to make an estimate for the total species numbers
- Sampling involves measuring small samples of a population that act to represent the whole population
Sampling
- Sampling is a method of investigating the abundance and distribution of populations
- There are two different types of sampling
- Random
- Systematic
- In random sampling the positions of the sampling points are selected at random
- This method avoids bias by the person that is carrying out the sampling
- Bias can affect the results, e.g.
- A student might choose to carry out samples in a particular location because it looks interesting, and this might give the impression that the habitat contains more species than it really does
- In systematic sampling the positions of the sampling points are located at fixed intervals throughout the sampling site
- This avoids accidentally missing out sections of habitat due to chance
- Systematic sampling allows researchers to investigate the effect of the presence of certain environmental features on species distribution, e.g. by taking samples along a line that extends away from an environmental feature such as a river
- A line of this type is known as a transect
- When a sampling area is reasonably uniform then random sampling is the best choice
- Random sample sites can be selected by
- Laying out a grid over the area to be studied
- Generating random number co-ordinates
- Placing sample sites in the grid squares that match the random number co-ordinates
Random & systematic sampling diagram
Random sampling involves selecting sample sites at random while systematic sampling involves placing sample sites at regular intervals
NOS: Students should be aware that random sampling, instead of measuring an entire population, inevitably results in sampling error
- A population estimate that is based on sampling makes the assumption that individuals are distributed evenly across the sample site, e.g.
- Random sampling may happen to miss an area of a site in which no individuals are present; this will result in an overestimate of population size
- Random sampling may happen to miss an area of a site where many individuals are present; this will result in an underestimate of population size
- There are many factors that influence the distribution of a population, so an even distribution is very unlikely, and so the chance of sampling error occurring when calculating such an estimate is very high
- A sampling error is the difference between an estimated population size and a true population size
- This occurs when a sample is not truly representative of a whole population
- Sampling error can be minimised by good investigation design, e.g. carrying out the right type of sampling and taking a large enough sample size
- When scientists write about their findings they must include details of any experimental methods used; this allows their readers to evaluate any error that may be present in the results