1. 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) 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.

3. Discuss Let’s start with a straightforward one: Is there a distinction between “nature” or “the natural world” and “science?” If so, what?

4. 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?

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. 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

7. 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…

8. 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

9. Orientation to the AP Curriculum Document - “Enduring Understandings”

10. 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.”

11. Scientific Inquiry What this means for you:  Creativity  Collaboration  Work  Frustration  Feelings of intimidation  Feeling lost or directionless  Independence  …Improved scientific reasoning skills

12. 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).

13. 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.

14. 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.

15. 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.

16. 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.

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

18. 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?

19. 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…”

20. 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!

21. 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.

22. 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

23. 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

24. 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

25. 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

26. 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.

27. 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) between hypotheses and predictions. Hypothesis: Your proposed explanation for the phenomenon.

28. 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.

29. 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?

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

31. 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.

32. 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?

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

34. 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?

35. 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!

36. 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

37. 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.  Discussion: Explain to your partner how to calculate/determine each of these five stats.

38. 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?

39. (Heads up) This symbol: …means “the sum of” For instance, what is 3, 5, 6?

40. 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, which is more helpful.

41. The formula:  Let’s say we measure 6 wingspans in centimeters: 2,2,2,5,8,12.

42. Standard Deviation Values: 2,2,2,5,8,12

43. 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 ____ of the mean (____) in our example are considered very normal for this particular data set.

44. 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?”

45. 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 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 in this data… Notice that this equation shows you, mathematically, that a bigger sample size = less standard error!

46. 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.”

47. 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: X 2 is just the symbol for this test, it does not actually mean x squared!

48. 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

49. Chi-Squared Test “How do I know what to expect?” It varies… Examples:  Suppose you want to know which of four bottles flies prefer.

50. 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.

51. 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.

52. 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?

53. Chi-Squared Test Null: Coin flips are purely chance.  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?

54. Chi-Squared Test EO Heads Tails

55. Chi-Squared Test I do the test, and it comes up heads 68 times and tails 32 times. These are my observed values. 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 is reasonable and let the mathematical chips fall where they may. EO Heads Tails

56. On to the calculation!

57. Chi-Squared Test “Observed,” 68 heads, 32 tails“Expected,” 50 heads, 50 tails Heads: + Tails: Chi square (X 2 ) = Sigma, or sum. I must complete these calculations twice: once for heads, once for tails, and then add them

58. 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). 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.

59. 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 (“that’s great, my mechanism may be real!”).  If your chi-squared value is LESS than or EQUAL to that number, your null hypothesis is SUPPORTED. Variation is likely random.

60. Chi-Squared Test.05.01.001 13.8416.63510.828 25.9919.21013.816 37.81511.34516.266 49.48813.27718.467 511.07015.08620.515

61. 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??

62. 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?

63. Chi-Squared Test You can use percentages 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?

64. Statistics Again: statistics like these don’t answer your question for you.  I’m more than 95% confident based on this single statistical evaluation 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. 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.

65. 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, (X 2 (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: ((X 2 (degrees of freedom, N=number of study subjects) = chi-square result, p<critical value).