1. The Nature of Science and Scientific Inquiry
AP Biology
The Nature of Science and Scientific Inquiry

2. 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.

3. 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?

4. PLORNT A summary of characteristics of science Predictable Logical
Observable
Replicable
Natural
Tentative

5. 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.

6. Biology
Biology has many subdisciplines, and different authors ascribe it different themes
Read 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…

7. Emergent Properties - World has a hierarchical organization
Emergent Properties - World has a hierarchical organization. New properties emerge as organization levels go up because of interactions between lower levels
The Cell - Basic unit of structure and function for life.
Heritable Information - DNA contains the biological information necessary for the continuity of life

8. Structure/Function - Form and function are always correlated
Interaction with the environment - Organisms are open systems that interact with the nonliving and living parts of their environment

9. Regulation - Feedback mechanisms regulate biological systems and maintain homeostasis
Unity and Diversity - Life comes in many diverse forms, however they all share a unity of common characteristics
Evolution - Explains unity and diversity. Natural selection explains the adaptation of species to their environments

10. Scientific Inquiry - Observation-based discovery and testing through hypothesis-deductive methods give credibility to observations and tests
Science, Technology, & Society - Technologies are goal-oriented applications of science

11. Hierarchy of Structural Levels in Biology
Molecule
Organelle
Cell
Tissue
Organ
Organ System
Organism
Population
Community
Ecosystem
Biome
Biosphere

12. 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.

13. 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.

14. Characteristics of Life RAREHOG
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

15. 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.

16. Regulatory Mechanisms Control Rxns
Negative and positive feedback are used in tandem to maintain homeostasis or a stable internal environment within living things.

17. 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.”

18. Scientific Inquiry What this means for you: Creativity Collaboration
Work
Frustration
Feelings of Intimidation
Independence
…Improved scientific reasoning skills

19. 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

20. 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.

21. 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.

22. 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.

23. 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.

24. Traditional Sci. Meth. ObservationsQuestions HypothesisPrediction
Test/ExperimentConclusion
Uses controlled experiments with only one experimental variable

25. 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?

26. 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…”

27. Standard Deviation:
A measure of how spread out the data is from the mean

28. Lower standard deviation:
Data is closer to the mean
Greater likelihood that the independent variable is causing the changes in the dependent variable
Higher standard deviation:
Data is more spread out from the mean
More likely factors, other than the independent variable, are influencing the dependent variable

29. 68% of data fall within ±1s of mean
σ = standard deviation
68% of data fall within ±1s of mean
95% of data fall within ±2s of mean
99% of data fall within ±3s of mean

30. The magnitude of the standard deviation depends on the spread of the data set
Two data sets: same mean; different standard deviation

31. Actual data sets aren’t always so pretty...

32. Calculating standard deviation, s
Calculate the mean (x)
Determine the difference between each data point, and the mean
Square the differenes
Sum the squares
Divide by sample size (n) minus 1
Take the square root

33. Standard Error:
Indication of how well the mean of a sample (x) estimates the true mean of a population (μ)
Measure of accuracy, if the true mean is known
Measure of precision, if true mean is not known

34. Accuracy – How close a measured value is to the actual (true) value
Precision – How close the measured values are to each other.

35. Calculating Standard Error, SE
Calculate standard deviation
Divide standard deviation by square root of sample size

36. How do we use Standard Error?
Create bar graph
mean on Y-axis
sample(s) on the X-axis
chemical 1 mean = 30 cm
chemical 2 mean = 50 cm

37. Add error bars!
± SE
Indicate in figure caption that error bars represent standard error (SE)

38. Analyze! Look for overlap of error lines:
If they overlap: The difference is not significant
If they don’t overlap: The difference may be significant

39.   Which is a valid statement?
Fish2Whale food caused the most fish growth
Fish2Whale food caused more fish growth than did Budget Fude  

40. Statements:
In all four regions, more males exhibited the trait measured than did females.
More males in region 3 exhibited the measured trait than did females  

41.   Mean belief scores for misleading ads Statements:
vmPFC = damage to ventromedial prefrontal cortex
BDC = brain damaged comparison group
Statements:
The vmPFC group identified fewer ads as misleading than did the normal group
The BDC group identified more ads as misleading than did the normal group.
# of ads identified as misleading  

42. 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!

43. 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.

44. 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

45. 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

46. 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

47. 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

48. 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.

49. 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.

50. 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.

51. 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?

52. Predictions
Prediction: The data that will result if the hypothesis is correct.
A well-written prediction will clearly set hypotheses apart from each other.

53. 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.

54. 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?

55. Variables Review: Independent (“Manipulated”) variable
Dependent (“Responding”) variable
Control variable
Control group

56. 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?

57. 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!

59. 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

60. 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.

61. 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?

62. 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.

63. Standard Deviation Values: 2,2,2,5,8,12 (2-5.16)2 = 9.99
(5-5.16)2 = 0.03
(8-5.16)2 = 8.07
( )2 = 46.79
= ( )/6 = 5.16
= 6

64. Standard Deviation Values: 2,2,2,5,8,12 (2-5.16)2 = 9.99
-> Sum = 84.86
(5-5.16)2 = 0.03
(8-5.16)2 = 8.07
( )2 = 46.79
= ( )/6 = 5.16
= 6

65. Standard Deviation Values: 2,2,2,5,8,12 √16.97 = 4.12 = s
(2-5.16)2 = 9.99
(2-5.16)2 = 9.99
(2-5.16)2 = 9.99
-> Sum = 84.86
(5-5.16)2 = 0.03
= 16.97 5
(8-5.16)2 = 8.07
( )2 = 46.79
= ( )/6 = 5.16
= 6
√16.97 = 4.12 = s

66. 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.

67. 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 = 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!

68. 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 / cm.”
Don’t report both, there’s no point.

69. 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.

70. Chi-Squared Test Sigma, “the sum of” “Expected,” the
data expected based
on the hypothesis
“Observed,” the data you
actually collected

71. 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.

72. Chi-Squared Test “Observed,” 68 heads, 32 tails “Expected,” 50 heads,
(68-50)2 = …… /50 = 6.48
PLUS
(32-50)2 = ……… 324/50 = 6.48
Chi square = = 12.96

73. 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.

74. 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.

75. 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

76. 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??

77. 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?

78. 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?

79. 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.

80. 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).