Question
“The body weights of the chicks were measured at
birth and every second day thereafter until day 20.
They were also measured on day 21. There were four
groups of chicks on di erent protein diets.”
Which of the fours diets leads to the most body
weight gain?
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Mean Lines & Points by Diet Over Time
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200
300
0 5 10 15 20
Time (days)
Weight (grams)
Diet Diet 1 Diet 2 Diet 3 Diet 4
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Group Discussions
The aim is to push the limits of your thinking
• It is not about the right and wrong answers
• Link it to your experience and work
What needs to be known to make a “good” decision?
• What do you and don’t you trust?
• Think pragmatically!
You can collaborate with the other groups
• The idea is to have fun, learn and share
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Question
Context: Objective is to sell the chicks based on their weight.
Question: Which of the four diets leads to the most body
weight gain?
• Is the question objective, unbiased and answerable?
• Can you think of a better question or questions?
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Data
Discuss whether the data is appropriate and valid to answer
the question?
What is the quality of the data and how was it processed?
Hints:
• What bias reduction measures were taken (e.g. when
assigning chicks to diets)?
• How and when were measurements made (e.g calendar
time and feeding times)?
• What quality control measures were in place (e.g. when
weighting chicks)?
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Analysis
Discuss the summary statistics, graphs and 95% con dence
intervals presented
Hints:
• Are the results generalisable?
• What factors should you consider when tting a statistical
model?
• What assumptions would you make?
• Would you model the raw data, change from baseline or
the % change from baseline?
• What can you say about bias and uncertainty?
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Communication
What would you communicate?
Hints:
• What are your main messages?
• Which Diet would you recommend and why?
• What are the strengths and weaknesses of the study?
• Think of biases and the uncertainty
• Be open and transparent
• Can a “good” decision be made using this study?
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Round up: Critical Reviews
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Data - Chick Related
Were the chicks allocated randomly (i.e. randomisation)
Where the chicks treated equally?
• Fed at the same time with identical quantities of food
• Weighted at the same time at each weighing
• Same “living” conditions? Were the temperature and
humidity levels similar?
What was the species of chick? Were they the same?
• What was the gender distribution?
• Were there siblings? How were they handled?
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Data - Experiment Related
Were the experimenters trained in the same way?
Were the diets blinded? To whom?
• Was there a control diet?
When was the experiment conducted?
• Start and end dates? Diet groups run in parallel?
Were the weighing machine(s) calibrated and identical?
• How were weights recorded?
Were other variables collected (e.g. chick sex)?
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Analysis - Presented in Slides
Raw values, change from baseline or % change from baseline
• All should lead to similar conclusions given that the
baseline values are around 40g for all chicks
• Raw values are best as chicks will be sold by weight
Assumptions:
• The data is a randomly selected sample that represents
the general population of chicks
• Independence between chicks (clearly not true for siblings)
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Analysis - Thoughts for Modelling
Weight over time is correlated for each chick
• Variable “Chick” must be part of “model structure”
• There could be correlation between chicks that are siblings
Modelling variables:
• Weight is the response variable
• Diet and Time are explanatory variables
• Chick is a “grouping” variable
How will chicks that dropped out (or died) be handled?
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Communication
Source data is not well characterised raising questions about
the quality and the possibility of bias
• Unknowns: diet allocation (randomised?), blinding,
experiment design and conduct (e.g. feeding & weighing)
• Quality control measure not speci ed hence uncertainty
increases
Other issues:
• Sample size seems to be small which increases uncertainty
• It is not clear if a control group was included
The results should be interpreted very cautiously!
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Summary
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Summary
Chick Weight Data is not reliable due to the unknowns
There are too many sources of bias and uncertainty
The results should be interpreted very cautiously!
• Too many risks!
Apply similar methods to your work
• Ask yourself the challenging questions before others do
• Be open and transparent
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This work is licensed under the
Creative Commons Attribution-NonCommercial 4.0
International License.
To view a copy of this license, visit
http://creativecommons.org/licenses/by-nc/4.0/
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