Experimental Design The basics

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Experimental Design - The basics

• Richard Preziosi

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How to formulate hypotheses

• Where do you start?
• What is a hypothesis?
• Stating a hypothesis
• Generating predictions
• Statistical hypotheses (different!)
• Only after completing this process will you be
  able to decide what data to collect

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Hypotheses Where do you start?

• Start by stating your research question
• E.g. Why are male and female humans different
  sizes?
• Your question may easily produce more than one
  hypothesis, thats fine.

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Hypothesis

• A hypothesis is a clear statement articulating a
  plausible candidate explanation for observations
• It should be constructed in such a way as to
  allow gathering of data that can be used to
  either refute or support the candidate explanation

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Stating a Hypothesis

• Phrase your hypothesis as a possible answer to
  your research question.
• E.g. Male and female size differ because males
  grow faster than females

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Generating predictions

• These are the testable statements that follow
  logically from your hypothesis
• E.g. males have a faster growth rate than
  females

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Statistical hypotheses

• Predictions should lead you to testable
  statistical hypotheses
• Note that the hypothesis of interest in
  statistics is the one where nothing is different
  (the null hypothesis)
• A clearly stated null hypothesis will generally
  lead you to the correct statistical test
• E.g. There is no difference in the growth rate
  of males and females

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Question
Hypothesis
Predictions
Statistical (Null) hypothesis
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Pitfalls of generating predictions

• Weak tests
• Indirect measures
• Non-useful outcomes
• Your tests must satisfy the devils advocate
  (e.g. reviewers or examiners)

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Weak test

• Consider the hypothesis Students enjoy the
  course in radiation training more than the
  workshop in experimental design
• Prediction Students will get better grades in
  radiation training than in experimental design
• This is a weak test (prediction) because other
  explanations are equally likely AND because we
  have used an indirect measure (grades as a
  measure of enjoyment)

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Non-useful outcomes

• These are hypotheses that may well prove
  interesting if true but are uninformative if false

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Satisfying skeptics

• Reviewers will look for logical flaws in your
  experiments. You do not want to finish your paper
  with
• My results indicate that mechanism A determines
  apoptosis rates. Although mechanism B could also
  produce the same response I believe that
  mechanism A is the important one
• This will earn you a review of the form
• This study provides no clear evidence to
  distinguish between mechanisms A and B. The
  authors need to redesign their study and start
  again. Recommendation, reject this manuscript

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Pilot Studies and Preliminary Data

• May be observational or mini-experiments
• Ensures sensible questions
• Can you observe the phenomenon?
• Practice and validate techniques
• Minimize training effects of data
• Recognize logistic constraints
• Standardization across observers
• Allows tuning of design and statistics
• Assessment of sample sizes (power)
• Test run of statistical analysis

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Experimental ManipulationVs. Natural Variation

• In Manipulation studies you change an aspect of
  the system and measure effects on traits of
  interest (majority of lab studies and
  Agricultural studies)
• In Correlational studies you measure associations
  between traits of interest (often assuming one is
  influencing the other) (Many Environmental and
  most Human studies)
• Consider the hypothesis Long tail streamers seen
  in many species of birds have evolved to make
  males more attractive to females

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Correlational study usingNatural Variation

• In the bird tail length example we could
• Measure the tails of males at the beginning of
  the breeding season
• Observe the number of matings each male has
• Do statistics to determine if there is a
  relationship between tail length and number of
  matings
• Results showing a relationship would support our
  hypothesis
• Results not showing a relationship would go
  against our hypothesis

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Manipulative study

• In the bird tail length example we could have 4
  groups of birds
• Results showing males with artificially long
  tails had more mates supports our hypothesis
• Results showing males with reduced tails had
  fewer mates also supports our hypothesis
• A comparison of group 1 males with the
  unmanipulated males acts as a control comparison

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Arguments for correlational studies

• Often less work (but larger sample sizes usually
  needed)
• Deals with real levels of biological variation
  (manipulations may take things outside naturally
  occurring limits)
• Requires less handling of organisms (important if
  there are constraints like stress to animals or
  endangered species)
• Manipulative studies may produce unintended
  effects (e.g. flight ability in example or
  epistatic effects in knockouts)
• Manipulation may not be possible
• May provide a baseline study manipulative expts.

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Arguments for manipulative studies(really,
against correlational studies)

• Third variables
• Reverse causation
• These can be BIG problems if they occur

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Third Variables

• Third variables occur when there is an apparent
  link between A and B but in fact there is no
  direct link or mechanism. Instead both A and B
  depend on C, the third variable.
• This means that patterns in correlations studies
  are just that, correlations.
• Remember, correlation does not imply causation

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Third Variables - an example

• In the bird tail length example lets say that we
  do see a correlation between tail length and
  number of mates
• Suppose that females are actually attracted to
  territories not males, but that males on better
  territories can grow larger tails
• The third variable here is territory quality and
  it drives both tail length and number of mates
  and produces an apparent relationship.

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Third Variables - Two famous examples

• Fisher suggested that the link between smoking
  and cancer was correlational not causative and
  that another factor, perhaps stress, led people
  both to smoke and develop cancer.
• Fewer women postgrads marry than women in the
  population as a whole. This relationship is
  presumable due to some other correlated factor
  (third variable)

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Reverse causation

• This occurs when it is assumed that correlation
  implies causation
• In some cases this can be ruled out based on
  other data or common sense
• In the bird example it is unlikely that the
  number of mates for a male has any effect on tail
  length measured at the start of the mating
  season.

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Reverse causation - a famous example

• There is a correlation between the number of
  storks nesting in chimneys and the number of
  children in a house (old data from Holland)
• Although storks bringing babies makes a nice
  story the causation is likely reversed
• Larger families tend to live in larger houses
  with more chimneys, and hence more opportunities
  for storks to nest.

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Variation, replication and sampling

• Variation among individuals
• Replication and the experimental unit
• Pseudoreplication

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Variation among individuals

• Variation among individuals is a given for most
  biological systems
• In any experiment we are concerned with variation
  in the Response or Dependent Variable
• Variation in the response variable can be divided
  into
• Variation explained by experimental factors (IV)
• Variation not explained by experimental factors
  (AKA error variation, random variation noise)
• In most studies we are interested in reducing
  noise and, hopefully, increasing explained
  variation

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Variation among individuals

• Single measurements from each treatment do not
  allow us to distinguish between noise and effect
• make sure you have a sufficient number of
  individuals that experience the same manipulation
• These individuals that receive the same
  manipulation are called replicates
• What is the experimental unit?

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Pseudoreplication

• This occurs when there is confusion between
  treatments, replicates and blocks.
• Consider an experiment comparing the effect of a
  toxicant on fish behaviour.
• Lets say the toxicant is prepared in a batch and
  drip fed into the treatments tanks (water is drip
  fed into the control tanks)
• Are the replicates
• Each fish in a tank?
• Each tank?
• Each set of tanks on a common drip?
• Each batch of toxicant?
• Dont expect a simple answer, the answer is in
  the biology, not in statistics

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Common sources of Pseudoreplication

• Shared enclosures
• Common environments
• Relatedness
• Pseudoreplicated stimulus
• Non-independence of group behaviour
• Pseudoreplicated measurements over time
• Species comparisons
• Sometimes pseudoreplication is unavoidable

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Random sampling

• Proper random sampling means that each individual
  has an equal chance of being allocated to each
  treatment group
• The problem with non-random treatment of samples
  is that any bias in assignment of individuals or
  systematic pattern to errors may bias your
  results
• True random samples almost always require the use
  of computers or random number tables

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Random assignment and treatment

• Random means not only random assignment but also
  random treatment
• Lets say that you are examining the effect of
  rhizosphere bacteria on plant growth.
• Not only should each plant have an equal
  opportunity of being assigned to the bacterial or
  non-bacterial (control) group all other aspects
  of the process should be random as well.
• Plants should be planted in equivalent compost
  (possibly in random order)
• Plants should be randomly allocated to growth
  chambers and perhaps positions in chambers

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Haphazard sampling

• Haphazard does not mean Random
• A haphazard sample is based on personal
  assignment by the experimenter in a fashion that
  they believe is random
• Often severely biased even if the experimenter is
  consciously trying to take a random sample
• Consider trying to randomly select mice from a
  bucket or randomly pippetting out aliquots of a
  cell culture
• True random samples usually involve setting up
  experimental units BEFORE assigning treatments
• BUT this is not always possible, use common sense
  (or blind assignment)

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Self selection

• This is a real problem with survey or poll data
• The subset of a population that respond to
  surveys is rarely a random sample and thus may
  bias your results
• By all means use surveys to inform your research
  BUT be very suspicious of anything but general
  conclusions

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Pitfalls of Random Sampling

• Make sure that the randomization procedure you
  use does what you intend
• Randomise the order of collecting data - learning
  effects
• Random samples Vs. Representative samples - dont
  let computers do your thinking for you

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Sample size - how many replicates

• Too few replicates can be a disaster - too many
  can be a crime!
• Always use educated guesswork - i.e. look at
  similar experiments by previous workers and
  determine what worked.
• Pay attention to differences between the studies
• Formal power analysis - do if possible!!!
• Requires that you have some guess of variation
  among replicates
• Requires that you have an idea of how big of a
  treatment effect you can expect (or require)
• Requires that you know what statistical test you
  will use

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Sample size - Resource Equation Model

• Can be used for complex studies or when variation
  among individuals is unknown
• Only appropriate for quantitative data
• Gives conservative estimates of sample size so
  more appropriate for
• Large effect size (e.g. lab rather than clinical)
• Testing for significant effects rather than
  estimating parameters
• E N - T - B
• N is the total number of individuals -1
• T is the number of treatments -1
• B is the number of blocks -1
• E is the error df and should be between 10 and 20
• In some cases E should be larger (see Festing et
  al.)

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Sample size optimization (Festing et al.)
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Controls

• This is the reference against which the results
  of an experimental manipulation can be compared
• Thus your control group should be identical to
  your treatment group in everything except the
  treatment itself
• Simple concept, common mistake
• If the predictions and statistical hypotheses
  have been constructed well then the control group
  will be obvious
• Lack of a control group makes an experiment
  pointless

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Types of Controls

• Negative control - unmanipulated
• Positive control - manipulated but not treated
  (vehicle control, sham procedure control)
• Concurrent control - run at the same time as the
  treatment group
• Historic control - based on previous data (be
  certain that individuals are identical except for
  the treatment)

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Blind Procedures

• Designed to remove the perception that
  unconscious bias might taint results
• Particularly useful when response variables are
  measured in a subjective way
• Blind Procedure - person measuring has does not
  know what treatment has been applied
• Double Blind - Both the subject and the person
  measuring does not know the treatment assigned
  (human studies)

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When controls are not needed (or allowed)

• In medical or veterinary studies controls may be
  an ethical issue, Historical controls can be used
  but give careful consideration to criticisms
• When sets of treatments are being compared (e.g.
  effect of two drugs on rat behaviour)

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Factorial experiments

• 2 group comparison (t-test) design
• Treatment and control compared
• 1 factor design
• Control and several levels of treatment compared
• 2 factor design
• More than one treatment considered simultaneously
• Allows estimation of both main effects AND the
  interaction between them

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Main effects and interactions

• Food Strain Interact

X - -
X X -
X X X
X X X
X X X
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Main effects and interactions
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Completely randomized designs Vs. Blocking

• Completely Randomized designs are usually simple
• Completely Randomized designs assume small among
  individual variation
• If among individual variation can be attributed
  to a known factor then you can BLOCK by that
  factor, reduce error variation and increase your
  signal to noise ratio (clearer results)

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Advantages of blocking
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Advantages of blocking

• Blocking is commonly used to remove effects of
• Space
• Time
• Individual characters that can be ranked
• Continuous characters that effect among
  individual variation can be used as covariates to
  remove effects and improve signal to noise ratio

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The most common design errors

• Ad hoc designs
• Inappropriate control/treatment groups
• Sample sizes too large or too small
• Failure to use blocking
• Lab animal studies failure to use isogenic
  strains when GxE unimportant