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) Some AP questions do not ask you to remember

anything; 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?

PLORNT

A summary of characteristics of sciencePredictableLogicalObservableReplicableNaturalTentative

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)

IncreasingComplexity

Biology

Biology has many subdisciplines, and different authors ascribe it different themesRead Ch. 1 for a fairly traditional list of themes

- particularly note “emergent properties,” it’s probably least familiar to you

BUT the AP Biology curriculum is organized differently…

Big Ideas

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”

1. Emergent Properties - World has a hierarchical organization. New properties emerge as organization levels go up because of interactions between lower levels

2. The Cell - Basic unit of structure and function for life.

3. Heritable Information - DNA contains the biological information necessary for the continuity of life

4. Structure/Function - Form and function are always correlated

5. Interaction with the environment - Organisms are open systems that interact with the nonliving and living parts of their environment

6. Regulation - Feedback mechanisms regulate biological systems and maintain homeostasis

7. Unity and Diversity - Life comes in many diverse forms, however they all share a unity of common characteristics

8. Evolution - Explains unity and diversity. Natural selection explains the adaptation of species to their environments

9. Scientific Inquiry - Observation-based discovery and testing through hypothesis-deductive methods give credibility to observations and tests

10. Science, Technology, & Society - Technologies are goal-oriented applications of science

Hierarchy of Structural Levels in Biology

Molecule Organelle Cell Tissue Organ Organ

System Organism Population Community Ecosystem Biome Biosphere

Emergent Properties

Properties of life come from complex organizations because of emergent properties or interactions between components.

When the parts are put together in a certain way, their functional properties emerge into something complex and new.

Dilemma of Reductionism Life exists because of emergent properties of

complex organizations.

When those organizations are taken apart to be studied, they no longer function.

Parts do not work outside of the whole, however it is impossible to study the whole without taking it apart.

Characteristics of LifeRAREHOG

Reproduction—life only comes from life (biogenesis) Adaptation, Evolution—life evolves as a result of

interaction with the environment Response to the Environment—react to the actions

going on around life Energy Utilization—Life takes in energy and transforms

it to do many types of work. Homeostasis—Maintain a stable internal environment Order/Organization—emergent properties come from

highly ordered structures Growth and Development—DNA directs growth and

development or changes within an organism that is characteristic of that species

Regulatory Mechanisms Control Rxns Enzymes, or biological catalysts, speed up

chemical reactions. These enzymes are controlled by feedback

mechanisms in which an output or product of a process regulates that process. Negative feedback or feedback inhibition

slows or stops a process when too much product is available.

Positive feedback can speed up a process when product is available.

Regulatory Mechanisms Control Rxns Negative and positive feedback are used

in tandem to maintain homeostasis or a stable internal environment within living things.

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 Independence …Improved scientific reasoning skills

Lab Groups Lab groups work best if there’s someone for each of these

roles: “Traffic director” comfortable with keeping everyone on-task,

limiting tangents, and with encouraging domineering speakers to take a step back and reluctant speakers to play more cards

Good with challenging others, questioning them, ask why why why, play devil’s advocate on everything

Responsible and takes initiative with respect to contacting the rest of the group outside of school

Someone who’s there for sunshine, has a good time and is good at making sure everyone’s having fun

At least one person in the group should be a “big picture” thinker, and one should be good at catching details

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.

Reasoning can be… Inductive: Reasoning from a set of specific

observations to reach a generalized conclusion. A generalization that summarizes observations

Deductive: Reasoning flows from general to specific. Predictions about what outcomes of experiments or observations are expected if a particular hypothesis is correct.

Scientific Method

The “scientific method” is flexible and creative as part of its power. But a study must still be logical, evidence-based, carefully organized etc. regardless of its form.

Traditional Sci. Meth.ObservationsQuestionsHypothesisPredictionTest/ExperimentConclusion

Uses controlled experiments with only one experimental variable

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, dimensions, duration, mass, etc.)

Qualitative = data that is non-numerical, observed but not measured. (Ex.: color, health, 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…”

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 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?

“British” birds

“African” birds

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

Scientific Questions

Based upon your previously-formed observations, design a scientific question that you will answer.This does entail thinking ahead to

experimental format.Everyone in the group works on the same

question, come to a consensus.

Hypothesizing

Hypotheses are tentative, initial ideas about experimental outcome based on your prior knowledge.

There is an important difference (gets down to the philosophy of science)

Hypothesis: Your proposed explanation for the phenomenon.

Hypothesizing

Two kinds of hypotheses: Null 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.

Your 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 a hypothesis, a sound idea about the answer to your question. You have a strong experimental protocol that will collect well-structured data… but how will you know if your hypothesis was probably right? How do you know whether or not the data support it?

Predictions

Prediction: The data that will result if the hypothesis is correct.A well-written prediction will clearly set

hypotheses apart from each other.

Hypotheses and Predictions

Generate your hypotheses and predictions. Be sure you’re able to justify your prediction, i.e. justify the kind of data you’ve chosen to evaluate your hypothesis. Your hypothesis and prediction can be different from

others in the group, but get their input to make sure that yours is sound, both in principle and in phrasing.

Theory vs. Law

You won’t be generating theories or laws, but you’ll be working with them.What’s the difference between a theory and a

law? How are these terms different as used in science vs. as used in layman’s terms?

Variables

Review: Independent (“Manipulated”) variableDependent (“Responding”) variableControl variableControl 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 maximizes the statistical significance of your results, while still being logistically feasible

What can you do to improve the statistical significance of your data?

Fair test

Design a fair test of your question. Whole group uses the same procedure. Produce a written, step-by-step procedure (make sure

everyone has it in your own notebooks) Be able to justify each step of your experimental protocol For this lab, you are required to generate both qualitative

and quantitative data If you’ll need materials other than the choice chamber,

determine who will acquire them, make sure your “reminds everyone” specialist has 2+ ways to contact them, and coordinate!

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 your results Answer your question

Statistical Analyses

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.

Statistical Analyses

A particular problem that statistics can help you to address is the significance of your results.How reliable is your sampling?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?

Standard Deviation Standard deviation is a measure of how diverse

your values are. That’s not generally very helpful at the AP level. More importantly, it will be necessary to know to calculate your standard error, which IS frequently helpful.Let’s say we measure 6 wingspans in centimeters:

2,2,2,5,8,12.

Standard Deviation

Values: 2,2,2,5,8,12

= 6= (2+2+2+5+8+12)/6 = 5.16

(2-5.16)2 = 9.99

(2-5.16)2 = 9.99

(2-5.16)2 = 9.99

(5-5.16)2 = 0.03

(8-5.16)2 = 8.07

(12-5.16)2 = 46.79

Standard Deviation

Values: 2,2,2,5,8,12

= 6= (2+2+2+5+8+12)/6 = 5.16

(2-5.16)2 = 9.99

(2-5.16)2 = 9.99

(2-5.16)2 = 9.99

(5-5.16)2 = 0.03

(8-5.16)2 = 8.07

(12-5.16)2 = 46.79

-> Sum = 84.86

Standard Deviation

Values: 2,2,2,5,8,12

= 6= (2+2+2+5+8+12)/6 = 5.16

(2-5.16)2 = 9.99

(2-5.16)2 = 9.99

(2-5.16)2 = 9.99

(5-5.16)2 = 0.03

(8-5.16)2 = 8.07

(12-5.16)2 = 46.79

-> Sum = 84.86

5= 16.97

√16.97 = 4.12 = s

Standard Deviation

What does this mean? 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.

Numbers within 4.12 of the mean (5.16) in our example are considered very normal for this particular data set.

“Three standard deviation rule” = More than 99% of the data points you could obtain will be within three standard deviations of the mean.

Standard Error 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.” Simple equation: standard deviation divided by the square root

of the sample size. (SE = s / √n) Standard error in our previous example =

4.12 / √6 = 1.68. Our mean wingspan was about 1.68 cm off from what we’d mathematically anticipate to be the real-world wingspan. Real-world mean wingspan is likely to be somewhere between 2.44 cm and 5.80 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 in this data…

Notice that this equation shows you, mathematically, that a bigger sample size = less standard error!

Reporting

Ways to report standard deviation and standard error in writing: Reporting standard deviation: “The total length of

wingspans (n=6) averaged 5.16 cm (s = 4.12).” Reporting standard error (I recommend this approach

usually): “The total length of wingspans (n=6) averaged 5.16 +/- 1.68 cm.”

Don’t report both, there’s no point.

Chi-Squared Test

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.

Chi-Squared Test

Sigma, “the sum of”“Observed,” the data youactually collected

“Expected,” thedata expected basedon the hypothesis

Chi-Squared Test Example, let’s test the hypothesis that a coin is weighted

towards heads. Null hypothesis: Coin flips are purely chance. If I flip the coin 100 times, and the hypothesis is correct, it should

come up heads 50 times and tails 50 times. I do the test, and it comes up heads 68 times and tails 32 times.

Chi-squared analysis can help me determine whether that variation is due to chance, i.e. 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. An oft-cited magic number is 30, but run as many trials as is reasonable and let the mathematical chips fall where they may.

Chi-Squared Test

“Observed,” 68 heads, 32 tails “Expected,” 50 heads,50 tails

(68-50)2 = 324 …….. 324/50 = 6.48PLUS(32-50)2 = 324 ……… 324/50 = 6.48Chi square = 6.48 + 6.48 = 12.96

Chi-Squared Test 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).

Critical value (or p-value): Basically, how certain you can be of your result. The industry standard p-value is .05, and if your chi-square

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 non-random.” 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 critical 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.

If your chi-squared value is GREATER than that number, your null hypothesis is REJECTED. You’ve supported your results as non-random.

If your chi-squared value is LESS than or EQUAL to that number, your null hypothesis is SUPPORTED. Variation is 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 12.96. 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. Null hypothesis: there is no preference.

Actual data: Of 147 fly visits that landed on fruit for at least 20 seconds, 48 flies spent at least 20 seconds on an apple, 87 flies spent at least 20 seconds on an orange, and 12 flies spent at least 20 seconds on a grapefruit.

Is this variation due to chance?

Chi-Squared Test

You can use means instead of counts. Try this problem: 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. Some of your flies spend more time on apples or oranges or grapefruits, others less. Altogether, they spend an average of 45% of their time on apples, 28% of their time on oranges, and 27% of their time on grapefruits.

Is this variation due to chance?

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, critical value (written as “less than” the p value), number of subjects (N), chi squared value. Round to two decimal places.

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.

Statistics

Again: statistics like these don’t answer your question for you. I’m more than 95% confident that the coin flips are non-

random, 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.

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).