1 / 10

Pseudoreplication and Ecology

Pseudoreplication and Ecology. Dr. James A. Danoff-Burg Columbia University. Purposes of Replication. Controls for random or stochastic error E.g., untested independent factors may otherwise determine the outcome of the experiment Increases the precision of the test

ulmer
Télécharger la présentation

Pseudoreplication and Ecology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Pseudoreplication and Ecology Dr. James A. Danoff-Burg Columbia University

  2. Purposes of Replication • Controls for random or stochastic error • E.g., untested independent factors may otherwise determine the outcome of the experiment • Increases the precision of the test • Increases the generalizability of the test • If you test across many sites – you can safely generalize to many others

  3. Some Definitions • Replicate = Sample • Maximize these in your experimental design • Greatest number possible, given logistical limitations • If you are a professional, use a power analysis • Subsample = Pseudoreplicate • Only true if the subsamples are incorrectly treated as true replicates for statistical analysis • Subsamples: useful to increase the accuracy of the data estimate for that replicate • A special type of statistical analysis are therefore possible

  4. Pseudoreplication - Defined • Incorrect “replication” • Replicating samples, not treatments • Replicates are not independent • Problem is that it violates a key assumption of statistical analysis: • Independence of replicates • Increasing precision of studies if independent • Approximates “truth” better if independent

  5. Prevalence of Pseudoreplication • 48% of all studies had pseudoreplication (Hurlbert 1984) • 71% of studies using ANOVA (a common statistical test) had design errors (Underwood 1981) • Particularly acute in studies with logistical problems • Rare animals • Transportational or financial limitations • Many of ours!

  6. Examples • Many samples from a single site • These are actually subsamples • Only a single sample for each treatment condition • These are actually replicates, but cannot do statistics on a sample size of one • Single samples from a single site, but replicated in time • Would be true samples if the experimental question is time-dependent • If not, it is pseudoreplication

  7. Pseudoreplication Example Treatment A Treatment B • Question – What is the affect of treatments A & B? • Pseudoreplication = treating stars of the same color as replicates • Replication = include only a single star of each color Site 3 Site 1 Site 2 Site 4

  8. Controlling Pseudoreplication I • Know your question • Question determines whether design includes pseudoreplication • Taxonomic level • Ecological hierarchy level • Clearly define your independent and dependent variables

  9. Controlling Pseudoreplication II • What constitutes a unit of data? • Plant branch? Individual? Population? Etc.? • Identify what is the unit of replication • Individual? Population? Community? Site? • Replicate accordingly – sites are often the level of replication for our projects • Randomize your sampling design • Helps to decrease sampling errors

  10. For Our Class • We will frequently use pseudoreplication • Limitations on time (only 4 weekends for our work!) • Limitations on transportation (only 1 van!) • Limitations on effort (only you!) • Consequently • We will frequently treat our pseudoreplicates as true replicates • However – be aware of this and you will be fine when you design more robust research projects in the future

More Related