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Experimental Design

sophie waddington 17 questions

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  1. 1Hypothetico-deductive reasoning:
    1. observation
    2. [blank_start]general[blank_end] hypothesis
    3. [blank_start]specific[blank_end] hypothesis
    4. test
    1. Ageneral
    2. Bspecific
  2. 2Statistical hypothesis tests test the [blank_start]null[blank_end] hypothesis, they do not prove they just support
    - the [blank_start]null[blank_end] hypothesis = predicts no effect/difference
    - [blank_start]alternative[blank_end] hypothesis = opposite to null, there is an effect

    Hypotheses must be formed by logical and rational reasoning and be: specific, predictive and [blank_start]test[blank_end]able
    - never proven or true statistical test provide evidence to support accepting or rejecting the hypothesis
    1. Anull
    2. Bnull
    3. Calternative
    4. Dtest
  3. 3Exploratory analysis:
    - do a [blank_start]pilot[blank_end] study to collect some preliminary data
    - use [blank_start]descriptive[blank_end] statistics to explore data (measures of average and variability)
    - spot mistaakes and outlying data point
    - look for [blank_start]patterns[blank_end] in the data, differences and relationships
    - then use this preliminary data to inform, revise and improve
    > hypotheses
    > [blank_start]study[blank_end] design
    > data collection
    > data [blank_start]analysis[blank_end]
    1. Apilot
    2. Bdescriptive
    3. Cpatterns
    4. Dstudy
    5. Eanalysis
  4. 4Examples of Descriptive Statistics:
    location, central tendency, average: mean, [blank_start]median[blank_end], mode

    spread/variability: standard [blank_start]deviation[blank_end], variance, coefficient of variation

    error/precision = [blank_start]standard[blank_end] error, [blank_start]confidence[blank_end] interval
    1. Amedian
    2. Bdeviation
    3. Cstandard
    4. Dconfidence
  5. 5Explanatory variable = the same as [blank_start]independent[blank_end] variable, "the thing having the effect" > always on the [blank_start]x[blank_end] axis

    [blank_start]response[blank_end] variable = the same as the dependent variable, "the thing you measure"
    1. Aindependent
    2. Bx
    3. Cresponse
  6. 6Data types

    1. [blank_start]nominal[blank_end] = single count of something in a category, categorical data)

    2. [blank_start]ordinal[blank_end] = numbers only indicate rank order

    3. [blank_start]interval[blank_end] data = numbers on a regular interval scale, can be continuous measurement or discrete counts
    - [blank_start]discrete[blank_end] = clutch size, number of parasites
    - continuous = height, weight
    1. Anominal
    2. Bordinal
    3. Cinterval
    4. Ddiscrete
  7. 7Samples and Populations
    - cannot measure every individual in a population, so we take measurements from a sample
    - use the sample to make predictions about the [blank_start]population[blank_end]

    [blank_start]biological[blank_end] population = collection of individuals of the [blank_start]same[blank_end] species at a particular location at the same time

    [blank_start]statistical[blank_end] population = individuals within a specified time or space about which inferences are to be made, they are potential [blank_start]sampling[blank_end] units

    [blank_start]variable[blank_end] = characteristic of a population which can differ from individual to individual

    observation = measurement provided by each sampling [blank_start]unit[blank_end] in a sample
    1. Apopulation
    2. Bbiological
    3. Csame
    4. Dstatistical
    5. Esampling
    6. Fvariable
    7. Gunit
  8. 8experimental study designs are better than observational studies for you are better able to establish cause and effect
    1. ATrue
    2. BFalse
  9. 9Sampling Techniques
    - [blank_start]field[blank_end] sampling > measurements, count data, transects, quadrats, mark/recapture, nearest neighbour, diversity

    [blank_start]laboratory[blank_end] sampling > specific equipment, control variables e.g. light, temp

    [blank_start]desk[blank_end]-based sampling > questionnaires, interviews, comparative studies, simulation
    1. Afield
    2. Blaboratory
    3. Cdesk
  10. 10Sampling methods
    - simple [blank_start]random[blank_end] sampling > affected by variability in small samples; each unit within the population must have an [blank_start]equal[blank_end] chance of being chosen

    - [blank_start]systematic[blank_end] sampling > uses information about the population to select a more 'representative sample', starts at a random point and select every nth sampling unit from an ordered list, affected by clumped/grouped sampling units

    cluster sampling = affected by clumped/grouped samples

    stratified [blank_start]sampling[blank_end] = allows sampling of homogenous subgroups
    1. Arandom
    2. Bequal
    3. Csystematic
    4. Dsampling

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first lecture of module
Language
English
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