Intro to Data & Experimental Design

Discuss: What’s missing?

Answer: Experimental Design and Data Collection!

1. Data Planning (Experimental Design)
   • Pilot Studies (Micro. Ver. of #2-4 below)
2. Data Collection (Experiment/Field Study)
3. Data Cleaning/Curation (e.g. remove missing values, units)
4. Data Exploration & Analysis
   • Data Validation (sanity checks, e.g. values make biological sense?)
   • Data Munging/Wrangling (raw -> processed)
   • Data Analysis (Statistics)
   • Data Visualization
5. Data Dissemination (Data Communication)

“Modern statisticians are familiar with the notion that any finite body of data contains only a limited amount of information on any point under examination; that this limit is set by the nature of the data themselves, and cannot be increased by any amount of ingenuity expended in their statistical examination: that the statistician's task, in fact, is limited to the extraction of the whole of the available information on any particular issue.”

- R. A. Fisher (biologist!)

There is desired and undesired information in data.

Goals:

• Get accurate information by reducing bias (do we have the right signal?)

• Get precise information by reducing sampling error due to random variation (increase signal-to-noise ratio)

  “An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem.”
  
  - John Tukey

For your question, there is desired (signal) and undesired (noise) information in your data.

Goals:

• Isolate desired information by reducing or controlling for confounding factors (i.e. undesired information)

“The aim … is to provide a clear and rigorous basis for determining when a causal ordering can be said to hold between two variables or groups of variables in a model…”

- H. Simon

“Designing experiments is as much about learning to think scientifically as it is about the mechanics of the statistics that we use to analyse the data once we have it. It is about having confidence in your data, and knowing that you are measuring what you think you are measuring. It is about knowing what can be concluded from a particular type of experiment and what cannot.

- Ruxton & Colegrave

Design your experiment so that:

• Measurements lead to useful data.
• Useful data has information addressing your hypothesis.
• Statistics are tailored to your data and powerful enough to separate out signal from noise.
• Results of statistics can be properly interpreted as evidence for or against your original hypothesis.

“It might be said that the two major goals of designing experiments are to minimize random variation and account for confounding factors.

- Ruxton & Colegrave

Definition: Random variation is the differences between measured values of the same variable taken from different experimental subjects.

Good experiments minimize or control for "unwanted” random variation, so that any variation due to the factors of interest can be detected more easily.

“It might be said that the two major goals of designing experiments are to minimize random variation and account for confounding factors.

- Ruxton & Colegrave

Definition: If we want to study the effect of variable A on variable B, but variable C also affects B, then C is a confounding factor.

Q1.1 If we wanted to measure the prevalence of both left-handedness and religious practices among prison inmates, what population would we sample from?

Q1.2 If we find that two people in our sample have been sharing a prison cell for the last 12 months, will data from them be independent?

Q1.3 If we are interested in comparing eyesight between smokers and non-smokers, what other factors could contribute to variation between people in the quality of their eyesight? Are any of the factors you have chosen likely to be related to someone's propensity to smoke?

Q1.4 Faced with two flocks of sheep 25 km apart, how might you go about measuring sample masses in such a way as to reduce or remove the effect of time of measurement as a confounding factor?

“Designing effective experiments needs thinking about biology more than it does mathematical calculations.”

“Experimental design is about the biology of the system, and that is why the best people to devise biological experiments are biologists themselves.”

- Ruxton & Colegrave

Whitlock & Schluter, Chapter 1 (PDF will be posted on Blackboard after class)