the PowerPoint presentation

The Hows and Whys
Driven to Discover
Enabling Student Inquiry
through Citizen Science
Sampling: to collect data on a subset of a
From samples we make inferences about the
overall population.
Sample size: as the fraction of the total
population included in the sample rises, the
greater the confidence the researcher can have
in the accuracy of the inferences.
Why sample?
 Unless you plan on measuring every
individual you will need to select a subpopulation for investigation
Why sample?
 Unless you plan on measuring every
individual you will need to select a subpopulation for investigation
 Field of milkweed: how tall are they
 Measure them all?
A complete count may be possible here….
But not here. Sampling may be needed. This
situation is more common in science.
Why sample?
Unless you plan on measuring every individual you will need
to select a sub-population for investigation
Random or structured?
 Samples can be selected in either a random
or a structured manner.
 Some studies and sites lend themselves to one
or the other
 Deciding random vs. structured is one of your
first major decisions.
Every n’th bush, bird, etc
Important that the starting point be randomly
chosen, however.
 This is not random, but structured.
 The “nth” determined by fraction of total
population you want included.
Plots at a set distance and compass
 eg. every 20 meters in a N-S/E-W grid
 The starting point can be
 logically chosen: the gate we walk
though, or 20 feet in from the corner of
the site
 Randomly chosen
 All bushes, birds, etc have an equal
chance of selection
 Draw numbers from a hat….
 Plots at a random distance and
compass heading
 Each iteration will be unique
 A combination of structured and random
selection. This is fairly common.
 Plots could be laid out along a transect at set
distances, but the direction of the transect could
be randomly determined.
NEVER! arbitrary
 Structured or random selection are OK, but it is
NEVER permissible to choose arbitrarily
 Arbitrary: one here, one over there, one by the
 Structured
 Defined rules for selection
 Random: no control or decisions by the researcher.
Usually with no rules*
 It is legitimate to require that no 2 random sites be within a set
distance of each other.
 Easy to confuse random and arbitrary, but they are
not the same.
Rules for both
 Regardless of whether you are using a
structured or random assignment, what you
get is what you get!
 Do NOT! skip a plot because it looks too
hard, or does not have any plants, and do
not choose points because they look “good”
When to use structured
Structured assignment of samples is good
sampling, and appropriate in some cases.
 Often used in bird surveys
 Also good if doing plots inside a single, larger
study site, especially if the plots will be
When to use random
 Random sampling is the gold standard
 Best if wanting to make inferences from a
population of discrete, obvious individuals
 Also used in identifying research sites
Stratified Sampling
Choosing random samples from 2 or
more classifications inside the
overall population
 Have equal numbers of men and women
 Select research sites of different classes
proportional to the ratio of that class
How to achieve randomness
 How you achieve randomness will depend
on project, here we assume random
allocation of plots
 Computerized maps to randomly choose plots
 Hand drawn and manually selected plots
 Random distance and direction
 GIS software--such as Arc-Map will allow
you to map your sites, define plots, or
choose random points
Hand drawn plots
 Map your study site on paper
 Break it into blocks
 Columns, rows
 Randomly select row and column (draw
numbers from a hat, or other random
 Number every potential plot, randomly
 Repeat until the needed number of plots is
Random distance and direction
 From a starting point (the center of the site, an edge, the
trailhead, or a random point itself) generate a random
compass heading.
 Spin a wheel, draw numbers 1-12 (the clock)
from a hat, spin a stick or pencil….
 Proceed in the direction indicated
 Repeat as needed.
 Distance can be standardized (or more
rarely also random
Random concerns
 Before starting you will need to define rules
for the plots
 How close can they be together
 Is stratification necessary
 How many will you need
 These need to be defined before starting
selection and should have a logical,
scientific rationale.
Sample Size
 As we discussed, larger samples relative to
the population will lead to more confident
 But also more work
 The key is to find a sample size large
enough to give you statistical significance.
 Ask the Monarch Lab or your science mentor
for help in determining adequate sample size.
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