Statistics Theory L3 = Experimental D Flashcards
Experimental design equation?
Experimental design = design structure + treatment structure + randomization
In environmental science studies, because we’re not designing a study in a carefully-controlled environment, what difficulties could arise? (3)
- Identifying the target population (statistical population), or matching the true statistical population to the intended statistical population.
- Using randomisation (assigning treatments to experimental units/random sampling).
- Using control or comparison areas or units.
Because we deal with a “treatment” (intervention/impact) that we can’t always control/manipulate, what consequences arise? (2)
- Might include long or broad time or spatial scales.
- Therefore, our only practical approach is to use observational studies (go to lesson 5).
To begin, do we have a clear definition/delineation of the target population?
No.
Why do we need a clear definition/delineation of the target population? (2)
It allows us to:
- Make easier decisions about the study design & sampling protocols.
- Easily apply results appropriately to the population of interest (intended popoulation).
Thing to note on Experimental designs?
There are various study types and the design of each study type will determine the inferences one can make from resulting the data.
Classification scheme/Classifications of research studies? (2)
- Studies of controlled events.
- Studies of uncontrolled events.
Types of Studies of controlled events? (3)
- Replicated experiments.
- Unreplicated experiments.
- Sampling for modelling (mu, beta, etc).
Types of Studies of uncontrolled events? (2)
- Perturbation (Intervention analysis - EIA).
- No perturbation.
Kinds of No perturbation studies? (2)
- Restricted domain of study (eg, particular age group).
- Sample over the entire domain of interest (complete population).
Philosophies for conducting research & making inferences? (3)
- Design-based/Data-based analysis.
- Model-based analysis.
- The two philosophies can be mixed, and rely on both approaches.
Design-based/Data-based analysis attributes? (2)
- Inferences are justified by the design of the study & the data collected.
- Inferences are dependent on several things.
What are the inferences of Design-based/Data-based analysis dependent on? (3)
- Appropriate y-variable.
- Methods to measure variables.
- A design protocol for sampling of the experimental set-up.
Model-based analysis attributes? (2)
- Rely on statistical models to make conclusions (eg, study of survival of cancer patients).
- Assumes that a valid inference depends on upholding model assumptions.
Even though we’ve already encountered tools such as randomisation, replication & control, how do we increase precision when dealing with limited replicates/absence of sufficient replication? (2)
- Blocking.
- Analysis of covariance (ANCOVA).
Blocking?
= randomly allocating treatments within homogenous groups (eg, sex).
Blocking attribute?
Is a categorical variable.
Analysis of covariance?
= includes covariates that might have an effect on the relationship of interest (eg, age in drug-cancer experiment).
Analysis of covarince attribute?
Is a continuous variable.
What is the goal of these additional methods, blocking & ANCOVA?
To help reduce the variability of the study.
Practical considerations? (7)
- Area of interest?
- Time of interest?
- Species/System of interest?
- Potential confounding/disturbing variables?
- Enough time to conduct the study?
- Budget?
- Magnitude of the anticipated effect?
Area of interest?
= what is the target/statistical population?
Time of interest?
= will it occur during day or night? different seasons? >1 year?
Species/System of interest?
= is it good for answering your question/solving the problem you want to solve?