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Experimental Design Workshop. Efficient and Effective Experiments Maximizing Information with Limited Resources Richard Preziosi Rob Shaw Norman Burton. Agenda. Why is design so important? The path from question to hypothesis: a statistical perspective Pitfalls and preliminary studies

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experimental design workshop

Experimental Design Workshop

Efficient and Effective Experiments

Maximizing Information with Limited Resources

Richard Preziosi

Rob Shaw

Norman Burton

agenda
Agenda
  • Why is design so important?
  • The path from question to hypothesis: a statistical perspective
  • Pitfalls and preliminary studies
  • Variation, sampling and replicates
  • Experimental designs to minimize error and improve power
please do not view this workshop as an isolated event
Please DO NOT view this workshop as an isolated event
  • Other training sessions
  • Individual consultations
  • Books
introduction
Introduction
  • What is Experimental Design?
    • Statistics!
introduction1
Introduction
  • What is Experimental Design?
    • Statistics!
    • Requires an appreciation of statistics
introduction2
Introduction
  • What is Experimental Design?
    • Biological insight!
    • Logic
    • Common sense
    • Planning
introduction3
Introduction
  • What is Experimental Design?
    • Biological insight!
    • Logic
    • Common sense
    • Planning
  • Note that there are different approaches to Experimental Design
some myths about ed
Some myths about ED
  • Myth 1
    • Its better to spend time collecting data than sitting around thinking about collecting data, just get on with it
  • Reality
    • A well designed experiment will save you tons of time. This belief often results in staff and post-docs sitting around while supervisors rewrite grant proposals and permit applications
some myths about ed1
Some myths about ED
  • Myth 2
    • “It does not matter how you collect your data, there will always be a statistical ‘fix’ that will allow you to analyse them”
  • Reality
    • NO WAY! This belief results in people crying in my office. Big problems are non-independence and lack of control groups.
some myths about ed2
Some myths about ED
  • Myth 3
    • “If you collect lots of data something interesting will come out and you will be able to detect even very subtle effects”
  • Reality
    • NO! Generally collecting lots of data without a plan wastes your time and someone’s money.
costs of poor design
Costs of poor design
  • Time is wasted. This is something you can’t afford and its sometimes downright embarrassing.
  • Money and resources are wasted. This is something your supervisor (or department or company) can’t afford and tends to make them quite angry.
costs of poor design1
Costs of poor design
  • Ethical issues (when animals or humans are experimental subjects)
    • Experiments must minimize the stress and suffering of any animals involved
    • Minimum numbers must be used
    • Experiments must have a reasonable chance of success
    • Ethical issues include causing damage or excessive disturbance to an ecosystem
    • Using poor design in animal studies is not only wasteful and embarrassing but may also be illegal
experimental design and statistics
Experimental Design and Statistics
  • Good experimental design is about more than statistics
  • You MUST know how you will analyse your experiment before you collect a single datum!
  • Once you have designed your experiment seek advice on the statistical test you will use.
  • Go ahead and use experienced people in your lab or department and/or a expert in statistics for this.
why is ed so important in life sciences
Why is ED so important in life sciences?
  • Biologists largely deal with complex, interacting systems (i.e. things that are alive OR composed of live parts)
  • Data are often non-independent
  • Random variation is common
  • Confounding factors are common
independence of data
Independence of data
  • Samples tell us about Populations
  • This is only true if the data in a sample are drawn randomly from the population
  • The true difficulty of non-independent data is that we do not know how it influences the sample (could be positively or negatively correlated)
slide16
Random variation(AKA between-individual variation, inter-individual variation, within-treatment variation or noise)
  • Found everywhere in biological systems
  • Important enough in biological systems that most modern statistics were invented by biologists
  • Note that this inherent randomness is not present in the data for all sciences
confounding factors aka confounding variables third variables
Confounding factors(AKA Confounding variables, third variables)
  • These are factors that interact with both what we are manipulating and what we are measuring
  • These need to be either eliminated through experimental design or have their effects removed statistically (covariates)