Science 1101
Science, Society, and the Environment I
Instructor Valerie King
Lecture 1 Outline
I. What is Science?
A. Forms of Scientific Inquiry
B. Types of Logic
II. Scientific Design
A. Scientific Method
B. Theory and Laws
III. Scientific Method in Action
A. Examples
B. Statistics
What is Science?
“Science” derived from Latin ‘to know’
 Way of asking and answering questions
 Seeking answers to questions about
natural phenomena (we are therefore
limited to what kinds of questions we ask)
 Scientific thinking reduces emotional
reactions

Forms of Scientific Inquiry

Discovery or Descriptive Science
–
–
Observation
Qualitative vs. Quantitative data
Types of Logic

Inductive Reasoning
–
Derive generalizations based on specific
observations
Types of Logic

Inductive Reasoning
–

Derive generalizations based on specific
observations
Deductive Reasoning
- Specific predictions follow from general
premise
Forms of Scientific Inquiry

Discovery or Descriptive Science
–
–

Observation
Qualitative vs. Quantitative data
Hypothesis-Based Science
Scientific Design
Scientific knowledge begins with an
observation and a proposed explanation.
 Explanation called a hypothesis
 A hypothesis is testable and falsifiable
 In science hypotheses are tested by using
them to make predictions about how a
particular system will behave

Example

Hypothesis: all objects fall when
dropped
–
–
Test this by dropping objects
Each object we drop is a test of our
prediction, the more successful tests the
more confidence in our hypothesis
 What
if we drop a helium balloon?
 What if we drop something in the
space shuttle in space?
 These are clear exceptions to our
original hypothesis-does this make
our hypothesis invalid?
Theories and Natural Laws
Theory: a description of the world that
covers a relatively large number of
phenomena and has met many
observational and experimental tests
 Law of Nature: theory (or group of
theories) that has been tested extensively
and seems to apply everywhere in the
universe-they become part of the
conceptual framework of a particular field

Scientific Method in Action
We use the scientific method in everyday
life
 Example:
You got in your car to drive up here and
turned the key but the car wouldn’t start
(observation)

Scientific Method in Action
Example:
You got in your car to drive up here and
turned the key but the car wouldn’t start
(observation)
Hypothesis: There is something wrong with
the car
Scientific Method in Action
Example:
You got in your car to drive up here and
turned the key but the car wouldn’t start
(observation)
Hypothesis: There is something wrong with
the car
Predictions: battery dead, ignition problem,
out of gas
Scientific Method in Action

Test predictions: turn on headlights, check
spark plug wires, dip stick in gas tank
Scientific Method in Action
Test predictions: turn on headlights, check
spark plug wires, dip stick in gas tank
 Analyze results: headlights work, strong
ignition spark, no gas on dip stick-gas
gauge reads half full

Scientific Method in Action
Test predictions: turn on headlights, check
spark plug wires, dip stick in gas tank
 Analyze results: headlights work, strong
ignition spark, no gas on dip stick-gas
gauge reads half full
 Draw conclusion: gauge inaccurate, out of
gas

Scientific Method in Action
I want to market a new flea collar for dogs
that is a natural remedy-no harsh
chemicals. But first I have to see if it really
works.
 Hypothesis: King’s collar repels fleas
 Prediction: dogs wearing the King collar
will have fewer fleas than dogs not wearing
the King collar

Important terms:
Independent (manipulated) variable:
condition or event under study (choose 1)
Dependent (responding) variable:
condition that could change under the
influence of the independent variable
(measure this)
Controlled variables: conditions which
could effect the outcome of the expt so
they must be held constant between
groups.
experimental group: group(s) subjected to
the independent variable
control group: group not subjected to the
independent variable, used as measuring
stick
reproducibility: producing the same result
consistently to verify result. It is therefore
important to describe your experimental
design in enough detail for others to
perform the same experiment.
Let’s recap
Hyp: King’s collar repels fleas
 Pred: dogs wearing King’s collar will have
fewer fleas than those without collar
IV: King’s collar
DV: presence of fleas
CV’s: anything that might effect the number
of fleas on the dogs
Can we think of some???

Experimental Design
• Obtain 500 dogs of various breeds from
local shelters. Have vet weed out the 200
dogs with the most fleas. Randomly
assign individuals to 2 groups.
• Board the dogs in identical environments
and treat them the same except that one
group gets to wear the King collar and the
other group does not
• After 2 wks. The dogs are examined by a
vet for fleas.
Results: the dogs wearing the King collars
were virtually free of fleas after the 2 wk
period compared to the dogs without the
collars which had about the same number
of fleas as when the experiment began
Second Example


Observation: polar bears are white, you
wonder why this is so
Hypotheses:
Match arctic landscape for protection/predation
White fur may reduce heat loss in warm-blooded
animals
Maybe polar bears are unable to produce melanin
therefore they are white as result
• Prediction: polar bears which are white
will capture more prey than those which
are dark
–IV: color of bears
–DV: number of prey
• Experimental Design: spray paint 5 polar
bears dark(experimental Group)/leave 5
white(control group) Track prey capture
for 8 weeks
Effect of Polar Bear coat color
on prey capture
Color of Bear
White
Number of Prey 12
Dark
7
Probability and Statistics
Probability: an attempt to measure and
predict the likelihood of an event
 Statistics: allow you to evaluate
comparisons between experimental and
control groups

Effect of Polar Bear coat color
on prey capture
Color of Bear
Mean Number
of Prey
captured
White
12
Dark
7
Mean: sum the values, divide by the
number of values
• Assumptions: factors thought to be true
for the investigation but have not been
verified or controlled
– Commonly accepted information
– Thought to be held constant but not controlled
– Factors beyond the investigators control
because of technical or time considerations
• Incorrect assumptions invalidate an
experiment!
• Assumptions:
1. All of the bears are equally hungry
2. Spray painting the bears has no effect
on their behavior etc. accept to make them
stand out on the ice
3. Our sample of bears is a good
representation of the polar bear population
in general
Statistics
Sample Size: # of observations necessary
to have a reliable representation of a
population
 Confidence Limits: estimates that reflect
the reliability of your mean (average)

–
Probability your sample is similar to other
random samples of that population