SCIENTIST MODULES 1.1–1.7 COMPREHENSIVE
STUDY GUIDE & REVISION NOTES 2026
Module 1.1 (How to Analyze a Scientific Argument)
Objective
Analyze a scientific argument to identify the claim, evidence, and reasoning
How to answer a question about the function of a living system: 2 alcoholic
drinks above the legal limit?
Scientists, including biologists, advance their knowledge of the world through
arguments
scientific arguments - a chain of reasoning that connects evidence to a claim
germ theory - microorganisms can cause disease (1546, an Italian doctorʼs
claim)
cell theory - all living things consist of cells (1830s a biologistʼs argument)
evolutionary theory - all organisms on Earth descended from a common
ancestor
claim - an assertion about truth
Is the claim clear and testable?
evidence - information related to the accuracy of a claim
Is the evidence relevant and sufficient?
reasoning - the process of logically relating evidence to a claim
Does the reasoning logically connect the claim to the evidence?
Module 1: How to Think Like a Scientist 1
, Module 1.2 (How to Support a Scientific Argument with
Quantitative Data)
Objective
A frequency distribution enables one to predict the probability of observing
specific values of a variable
minimum - the lowest value in a set of numbers
maximum - the highest value in a set of numbers
median - the central value in an ordered set of numbers
frequency - the number of times that a value within a range has been
observed
relative frequency - the proportion or percentage of times that a value within a
range was observed
Module 1.3 (How to Simplify Biological Data with a
Model)
Objective
Relate a frequency distribution to a probability distribution
A normal probability distribution can accurately describe a frequency
distribution
probability - a theoretical chance of observing a value within a range
probability distribution - a mathematical function that enables one to calculate
the probability of observing a value within any range
parameter - a constant in a mathematical function, whose value can be
estimated from data
Module 1: How to Think Like a Scientist 2