AP Biology
The Nature of Science and
Scientific Inquiry
First things first…

The role of discussion
 (Besides
the obvious importance of being a capable
thinker if you’re going to succeed in science)


All AP questions ask you to interpret data or a model in light
of what you know.
Some AP questions do not ask you to “remember” anything
at all; they give you a novel problem, and ask you to logic
your way through it
 Full
participation in discussions - no matter how
difficult or how silly the questions look - is crucial to
developing the intellectual capabilities this course
(and the AP exam) demand.
Discuss

Let’s start with a straightforward one: Is
there a distinction between “nature” or “the
natural world” and “science?” If so, what?
Discuss
We distinguish science from math, from
history, from poetry, etc. We call them
separate fields of study, but what are their
natures and what are their boundaries…
 What actually makes something
“science?” What characteristics must
something have in order for us to call it
science?

Discuss


There are different methods we have for trying to
understand the natural world (such as?)
Science has been called the “most powerful” of
those methods. What does “powerful” mean in
this context?
 Once
you feel satisfied with your answer to that, try to
tackle putting into words what about science gives it
that power.
Scientific Disciplines

Scientific disciplines are interrelated and
interdependent, and occur in levels of
“fundamental” (not cognitive) complexity
Physics (fundamental forces of material world)
Chemistry (nature and behavior of matter)
Space and Planetary Sciences
(non-living macroscopic phenomena)
Biological Sciences
(living things)
Increasing
Complexity
Scientific Disciplines
Biology

Biology has many subdisciplines, and
different authors ascribe it different themes
 Read
Ch. 1 for a nicely illustrated traditional
list of themes - particularly note “emergent
properties,” it’s probably least familiar to you
 BUT the AP Biology curriculum is organized
around just four themes…
Big Ideas

The Four “Big Ideas”
 Evolution
 Cellular
Processes
 Genetics and Information Transfer
 Interactions

Every topic that we study is connected to all
four, and you need to get accustomed to noticing
those connections as they come up

Orientation to the AP Curriculum
Document - “Enduring Understandings”
Scientific Inquiry

Major labs in AP Biology all feature inquiry
 Inquiry:
“The diverse ways in which scientists study
the natural world and propose explanations based on
the evidence derived from their work. Scientific inquiry
also refers to the activities through which students
develop knowledge and understanding of scientific
ideas, as well as an understanding of how scientists
study the natural world.”
Scientific Inquiry

What this means for you:
 Creativity
 Collaboration
 Work
 Frustration
 Feelings
of intimidation
 Feeling lost or directionless
 Independence
 …Improved scientific reasoning skills
Scientific Inquiry

What is the scientific method?

Just kidding… sort of. There is NOT one
scientific method. It’s an umbrella term for a
variety of different methods that are scientific
because they’re logical, naturalistic, and
evidence-based (remember PLORNT).
Types of Scientific Studies

Controlled experiments
 Scientist-generated
set up, the kind of experiment
you’re more familiar with.

Natural experiments
 Picking
your independent, dependent, control
variables, then going out and finding a situation that
already occurred/already exists with those variables
in place.

Field studies
 Emphasis
on inference from structured observation
rather than establishment of variables and controls.
Types of Scientific Studies

Thought Experiments
 Evaluates
a hypothesis by thinking through to its
consequences. Einstein’s are famous.

Mathematical Evaluation
 Using
math theorems to work out underlying
phenomena. Almost exclusive to physics. Can be
considered a form of modeling.

Modeling
 Using
physical models, as in chemistry, or computer
models, like weather models, to address questions.
The models are generated based on real-world data,
but the study you conduct doesn’t involve real-world
data itself.
Scientific Method

The “scientific method” is flexible and
creative as part of its power, but science is
not a free-for-all either!
A
study must still be logical, evidence-based,
carefully organized etc. regardless of its form.
Traditional Scientific Method

An observation is a description of
information gathered with one of your five
senses.
 It
is important not to conflate observation with
inference. Inference = ideas, assumptions,
conclusions.

Why is it important that observations be free of
inference?
Data

Your observations can yield two types of data:
 Quantitative
= data that can be measured.
Numerical. (Ex.: number of objects, length, duration,
mass, etc.)
 Qualitative
= data that is non-numerical, observed
but not measured. (Ex.: color, health, location, etc.)
 It’s
possible to turn qualitative data into quantitative
data and vice versa. For instance, ranking a reaction
speed on a scale of 0-5 rather than “very slow, slow,
medium…”
Lab Notebook







Write in pen
Make a title page with your name, the course (“AP Biology Laboratory
Notebook”), instructor name (“Instructor: Ms. Rebecca Stang”), the school &
school year (2014-2015)
(Either leave a page for Table of Contents, or plan to mark each lab with a
sticky tab)
On the next page, title it with the name of the lab at the top (Animal Behavior
Lab, this time)
As you proceed, always clearly title new sections. For instance, today, you
will be taking “Observations.” Later, “Brainstorming Questions,” “Final
Question,” “Hypotheses,” etc.
Always DATE when each new bit of writing begins. You can do this in the
margin or with the section title.
NEVER tear out pages, NEVER erase or scribble anything out! If you need
to make a change, draw a single line, so that the original text is still visible.

For an optional professional touch, number each page, and date any cross-outs
Observations

In your lab notebooks, make detailed
observations of these animals’ behaviors. You
may feel free to manipulate them, place them in
different environments, etc., but do NOT:
 Start
running an off-the-cuff experiment
 Let them be harmed

Detail! Avoid inference!
Movement

Animal movements can be kinesis or taxis.

A kinesis is a simple change in activity or
turning rate in response to a stimulus. It is
non-directional.
 For
instance, when humidity increases, wood lice
spend less time stationary. But they don’t move
towards or away from a human or moist area.
Movement

A taxis is a more or less automatic, oriented
movement toward or away from a stimulus.

Examples of taxis in animals include:
 Phototaxis
= movement toward/away from light
 Phonotaxis = …sound
 Chemotaxis = …a chemical
 Anemotaxis = …wind
 Trophotaxis = …food
 Geotaxis = …earth or gravity
 Magnetotaxis = …a magnetic direction
 Klinotaxis = …a slope
 Rheotaxis = …water currents
Discussion


“British” birds
“African” birds
Blackcaps generally
breed in SW Germany
and winter in Africa,
but some winter in
Britain.
Take both kinds of
bird, put them in
Germany, do a “peck
test” to determine flight
direction.
 What
kind of movement
is most likely being
demonstrated here?
Scientific Questions

Not all questions are scientific, and not all
scientific questions are conducive to a good
study. A question must be:
 Centered
on phenomena (objects, organisms, events)
in the natural world
 Connects to scientific concepts rather than opinions,
feelings, beliefs
 Possible to investigate through experiments and/or
observations
 Leads to gathering evidence and using data to
explain how the natural world works
Scientific Questions

Following these guidelines, meanwhile, isn’t
necessary for the question to be defined as
scientific, but will lead to a more productive
study:
 It’s
something you’re interested in finding out!
 You don’t already know the answer
 Shouldn’t be a “yes or no” answer
 Has a clear focus
 Is grounded in existing scientific understanding
 Is of a scope that matches the materials and setting
available
 Can lead to further questions once all data is
gathered
Hypothesizing


Hypothesis: A proposed explanation for
a natural phenomenon.
Hypotheses are small-scale models of
nature based on prior knowledge.

They are not guesses. They may be made
before a test, or they may be the result of a
test.
 There is an important difference (gets down
to the philosophy of science) between
hypotheses and predictions.
Hypothesizing

Two kinds of hypotheses:
hypothesis: The general or “default” condition,
the hypothesis that there is no relationship between
the variables, that the treatment does not have any
effect, etc.
 Alternate hypotheses: That there is a relationship,
effect, etc.
 Null



There may be multiple alternate hypotheses, but only one
null
Alternates may be “combined”
Your “favorite” hypothesis may be either null or alternate, but
be aware of both in order to be able to coherently explain
your experiment
Discussion

So you have hypotheses, you have a
strong experimental protocol that will
collect well-structured data… but how will
you know which hypothesis was probably
right? How do you know whether or not
the data support it?
Predictions

Prediction: A statement of the data that will
result if a given hypothesis is correct, and this
result would not be obtained if the hypothesis is
incorrect.
 Predictions
“coming true” are how you support or
refute hypotheses.
 Predictions can be directly verified/observed,
hypotheses (explanations) can’t.
 A well-written prediction will clearly set hypotheses
apart from each other.
Theory vs. Law

You won’t be generating theories or laws,
but you’ll be working with them.
 What’s
the difference between a hypothesis, a
theory, and a law? How are these terms
different as used in science vs. as used in
layman’s terms?
Variables

Review:
 Independent
variable
 Dependent variable
 Control variable
 Control group
Fair test

A fair test of your hypothesis is one that avoids
confounding variables - variables that damage
the internal validity of your study.
 The
easiest way to do this is often to ensure that
there’s only one independent variable, but that’s not
true of every study!
 A fair test also minimizes the chance of errors while
maximizing the statistical significance of your results,
while still being logistically feasible


What can you do to improve the statistical significance of
your data?
You may wish to conduct a pilot study – a
“pre-test” to confirm the protocol works
Statistical Analyses

What statistics CAN do:
 Quantify
your results
 Clarify your results
 Provide an additional representation of your results
 Provide additional evidence

What statistics CANNOT do:
 Evaluate
or interpret your results
 Answer your question
Statistical Analyses and Data
Representation

Basic operations: mean, median, mode, range,
rate
 Use
them whenever it’s appropriate, and don’t use
them when it’s not
 Does it help illustrate your point? Is it not necessary
to back up your point?

If you conduct an operation and it REFUTES the point you
were planning to make, not including it is dishonest, and a
real scientist could get in big trouble for that!
 Discussion:
Explain to your partner how to
calculate/determine each of these five.
Statistical Analyses

A particular problem that statistics can
help you to address is the significance of
your results.
 How
reliable is your sampling? (Standard
Deviation & Standard Error)
 How certain can you be that your data swing
that way because something drove it to? How
do you know your results aren’t random?
(Chi-Square Analysis)
Heads up: Sigma aka “Funky E”

This symbol:

…means “the sum of”

For instance, what is
3, 5, 6?
Standard Deviation (calculation not on AP exam)

Standard deviation is a measure of how diverse
your values are.
 That’s
not generally very helpful at the AP level, but
you need it for the next calculation.
The formula:
 Let’s
say we measure 6 wingspans in centimeters:
2,2,2,5,8,12.
Standard Deviation

What does this mean?
 For


AP Bio labs, not much…
The greater your standard deviation (especially as compared
to your mean), the greater your variation in data.
The more standard deviations a figure is away from your
mean, the more unusual it is compared to the rest of your
data.

Values within 4.12 of the mean (5.16) in our example are considered
normal for this particular data set.
Standard Error

(calculation not on AP exam)
Standard error indicates the average
difference between the data mean you
obtained from your limited number of trials,
and the calculated data mean in the “real
world.”
 “How
certain am I that my sample is
representative? If I’d done more trials, what could
my mean turn out to be instead?”
Standard Error
(calculation not on AP exam)

Simple equation: standard deviation divided by the square
root of the sample size. (SE = s / √n)

Standard deviation in our wingspan study was ___, and we
sampled 6 birds.
Standard error:.
Our mean wingspan (5.16 cm) was within ___ cm of what
we’d mathematically anticipate to be the real-world wingspan.
Real-world mean wingspan is likely to be somewhere
between __ cm and ___ cm.




That’s a pretty large standard error; our mean varies from the expected by
about 25%! Maybe we can’t necessarily be very confident that 5.16 is a
representative mean wingspan…
Notice that this equation shows you, mathematically, that a bigger sample
size = less standard error!
Reporting

Standard error can be useful to report in
some labs, but not always.
 Reporting
standard error in writing, include
the sample size, mean, and standard error:
“The thing being studied (n=sample size)
averaged mean +/- standard error.”

“Wingspan length (n=6) averaged 5.16 +/- 1.68
cm.”
Reporting (fair game on AP exam)

Standard error, when calculated, should be
represented in any relevant graphs using
“confidence intervals (CI)” or “error bars.”
 How
could you show SE +/- 1.0 on this graph?
Mean Height (cm)
Mean Hatchling Height by Diet
5
With sugar
Without sugar
Standard Error

You’ll notice newspapers abuse this
statistic CONSTANTLY.
 “Candidate
A is crushing Candidate B in the
polls with a lead of 4%! 52% of respondents
plan to vote for Candidate A vs. only 48%
voting for Candidate B (margin of error +/3%)”

……Sigh :/
Chi-Squared Test (IS tested!)

The chi-squared ( ) test, or Pearson’s
chi-squared test, evaluates the likelihood
that variation in your results was due to
chance.
 It
can’t tell you whether the variation was
because your independent variable caused it,
but it can be used as evidence to rule out a
null hypothesis.
Warning: this is just the symbol for this test, it does not actually mean x squared!
Chi-Squared Test
Sigma, “the sum of”
“Observed,” the data you
actually collected
“Expected,” the
data point you would get
if the null hypothesis
is correct
Chi-Squared Test
“How do I know what to expect?” It
varies…
 Examples:

 Suppose
you want to know which of four
bottles flies prefer.
If the null is true, they have no preference.
 In which case, you would expect them to spend
25% of their time in each bottle.

Chi-Squared Test
“How do I know what to expect?” It
varies…
 Examples:

 Suppose
you want to know which of two
flower colors, blue vs white, is more
advantageous in an environment. There are
300 flowers.
Chi-Squared Test
“How do I know what to expect?” It
varies…
 Examples:

 Suppose
you want to know which of two
forests a species of finch prefers. One forest
is 800 acres, the other is 200 acres. A finch’s
movements are tracked for 100 hours.
Chi-Squared Test

Let’s calculate chi-square! :D

Our question: the heads side of a coin seems to
have more mass to its image. As a
consequence, is a coin actually weighted
towards heads when you flip it?

What is the null hypothesis in this instance?
Chi-Squared Test

.
 When
I conduct this test, there will be two
“outcomes” I can get: heads, or tails.
 If
I flip a coin 100 times, and the null hypothesis is
correct, what are my expected values for each of
those two outcomes?
Chi-Squared Test

If I flip the coin 100 times, and the null hypothesis is
correct, it should come up heads 50 times and tails 50
times.
E
Heads 50
Tails
50
O
Chi-Squared Test

I do the test, and it comes up heads 68 times and tails 32 times.
E
Head 50
s
Tails 50
O
68
32

Chi-squared analysis can help me determine whether that variation from my expectations is due to
chance or due to something actually causing heads to be more frequent. i.e., it assesses whether
my null hypothesis holds any water.

You must have at least two possible outcomes in your experiment (heads and tails, here) for
the test to work.

Chi-square DOESN’T WORK if you don’t have enough data points/trials or if their
values are too small. An oft-cited magic number is 30, but run as many trials as possible, measure
as precisely as possible, and let the mathematical chips fall where they may.

No E can be 0.
On to the calculation!
You also need to know:

Degrees of freedom: The number of outcomes
minus 1
 In
our coin example, we have two outcomes being
tested, heads and tails. That gives us one degree of
freedom (2-1 = 1).

P-value: Basically, how certain you can be of
your result.
 The
industry standard p-value is .05, and if your chisquare works it, that amounts to “I am 95% positive
that this result is non-random.” A p-value of .01
amounts to “I am 99% positive that this result is nonrandom.” p-value of .001 is 99.9% certainty.

Use .05 in AP Bio.
Chi-Squared Test

Now that you have your chi-square, degrees of
freedom, and p-value, you’re nearly done. You
just need a chart of critical values.
 Find
your degree of freedom and your p-value in the
row and column headers. Read down and across to
find your cell with your critical value.
 If your chi-squared value is GREATER than the
critical value, your null hypothesis is REJECTED.
You’ve supported your results as non-random.
 If your chi-squared value is LESS than or EQUAL to
the critical value, your null hypothesis is
SUPPORTED. Variation is likely random.
Chi-Squared Test
.05
.01
.001
1
3.841
6.635
10.828
2
5.991
9.210
13.816
3
7.815
11.345 16.266
4
9.488
13.277 18.467
5
11.070 15.086 20.515
Chi-Squared Test

Our coin test gave us a chi-square of ___.
Does that support or reject the null
hypothesis?

Does this mean that the coin is definitely
rigged or definitely fair??
Chi-Squared Test

Try this problem:
 You’re
testing to see if fruit flies prefer different fruits:
apples, oranges, grapefruits. You set up a triangular
choice chamber with an apple in one corner, an
orange in another, and a grapefruit in the third.
 Null hypothesis: there is no preference.
 Actual
data: Of 147 fly visits that landed on fruit for at
least 20 seconds, 48 flies did so on the apple, 87 flies
did so on the orange, and 12 flies did so on the
grapefruit.

Is this variation due to chance?
Chi-Squared Test

Remember, your values must be sufficiently large to run
chi-square! Avoid this problem in experimental design
by using variables with the opportunity for high precision.
How about taking fruit fly data like this instead?



You’re testing to see if fruit flies prefer different fruits: apples,
oranges, grapefruits.
Null hypothesis: there is no preference.
Actual data: You release 30 flies into a container with three fruits
and clock how much time they spend on fruit. Altogether, they
spend a total of 418 seconds of their time on apples, 220
seconds of their time on oranges, and 187 seconds of their time
on grapefruits.

Is this variation due to chance?
Chi-Squared Test

Let’s do the same problem, with a twist!
data: Apple – 418 sec, Orange – 220
seconds, Grapefruit – 187 seconds
 But what if, in the choice chamber, there were
TWO apples in the apple corner, and still only
one orange and one grapefruit in their
corners?
 Same
Chi-Squared Test

If you reject the null hypothesis, your results can be
reported as “significant” or “statistically significant.”

When writing them up, you need to include all of the following:
degrees of freedom, p-value (written as “less than” the p value),
number of subjects (N), chi squared value.

For instance, I would write of our coin test:

Coin flips were found to be non-random in a chi-squared test, (X2 (2,
N=100) = 12.96, p<.05). From this, we can conclude that coin flips
were significantly weighted towards heads.

The contents of those parens were: ((X2 (degrees of freedom,
N=number of study subjects) = chi-square result, p<p-value).
Statistics

Again: statistics like these don’t answer your
question for you.
 Even
if I’m more than 95% confident based on this
single statistical evaluation that the coin flips are nonrandom, but it doesn’t mean the coin was rigged!
Maybe it was the way I flipped it, or air currents, or the
table shape, or something else. Or, maybe I’m
wrong!


The stats are like another data point, another piece of
evidence.
You have to engage your brain and interpret your statistics,
no differently than how you must interpret raw data. And a
crummy study design can give you great-looking statistics (or
terrible ones). A scientist would look at your mere 100 coin
flips and not place a lot of trust in that chi-square analysis.