1. Paul Strode Integrating Data Analysis with The Science Practices
Statistics is the most important tool in Biology
Paul Strode
Pre-IB/IB Biology/Science Research
Fairview High School, Boulder, CO
25th year teaching
I will then have you introduce yourself

2. Paul Strode Integrating Data Analysis with The Science Practices
Pre-IB/IB Biology/Science Research
Fairview High School, Boulder, CO
25th year teaching
University of Washington
M.Ed. Science Education
I will then have you introduce yourself
University of Illinois
Ph.D. Ecology and
Environmental Science
Manchester College (IN)
BS. Biochemistry
(now Manchester Univ.)

3. Integrating Data Analysis with The Science Practices
I will then have you introduce yourself

4. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices
2002
Fairview High School
64 students in IB/AP Biology
500 students in the Senior Class (13%)
87% of students may graduate with little, if any training in data and error analysis

5. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices

6. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices

7. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices

8. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices
2013

9. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices
2013
Schlotter, N. E A statistics curriculum for the undergraduate chemistry major. Journal of Chemical Education 90:51-55.

10. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices
Framework for K-12 Science Education (2011)
By grade 12, students should be able to:
Analyze data systematically, either to look for salient patterns or to test whether the data are consistent with an initial hypothesis.
Recognize when the data are in conflict with expectations and consider what revisions in the initial model are needed.
Use spreadsheets, databases, tables, charts, graphs, statistics...

11. Integrating Data Analysis with The Science Practices
Going Beyond the Mean: Statistics in the High School and College Science Classrooms
Integrating Data Analysis with The Science Practices
Chapter 3: Dimension 1: Scientific and Engineering Practices; Practice 4: Analyzing and Interpreting Data
The National Academies Press (2011), p. 3-30

12. Integrating Data Analysis with The Science Practices
2013 nextgenscience.org

13. Integrating Data Analysis with The Science Practices
MS.ETS1 Engineering, Technology, and Applications of Science
HS.ETS1 Engineering, Technology, and Applications of Science
2013 nextgenscience.org

14. The College Board gets a little more specific
Integrating Data Analysis with The Science Practices
The College Board gets a little more specific
The College Board (2015), p. 98

15. The College Board gets a little more specific
Integrating Data Analysis with The Science Practices
The College Board gets a little more specific
The College Board (2015), p. 98
There is a typo on your procedures

16. The College Board gets a little more specific
Integrating Data Analysis with The Science Practices
The College Board gets a little more specific
The College Board (2015), p. 98

17. Integrating Data Analysis with The Science Practices
Calculation of Variance, Standard Deviation, and using 95% Confidence Interval error bars to illustrate uncertainty in summarized data
The Student’s t-Test to compare two means
One-Way Analysis of Variance (ANOVA) to compare more than two means
The Chi-square Test to compare observed with expected distributions
The Pearson Product-moment Correlation Coefficient (r) and Linear Regression (r2) to determine the strength of a relationship
We can do ALL of this for SOME of our students and we can do SOME of this for ALL of our students.

18. Integrating Data Analysis with The Science Practices
Calculation of Variance, Standard Deviation, and using 95% Confidence Interval error bars to illustrate uncertainty in summarized data
The Student’s t-Test to compare two means
One-Way Analysis of Variance (ANOVA) to compare more than two means
The Chi-square Test to compare observed with expected distributions
The Pearson Product-moment Correlation Coefficient (r) and Linear Regression (r2) to determine the strength of a relationship
But we also must build a solid foundation:
The meaning of the hypothesis/explanation in science.
The difference between a hypothesis/explanation and a prediction.
The null statistical hypothesis (H0)
p-values
Degrees of freedom (df)

19. Integrating Data Analysis with The Science Practices

20. Integrating Data Analysis with The Science Practices

21. Integrating Data Analysis with The Science Practices
Toothpickase?

22. Integrating Data Analysis with The Science Practices

23. Integrating Data Analysis with The Science Practices

24. Integrating Data Analysis with The Science Practices

25. Integrating Data Analysis with The Science Practices

26. Integrating Data Analysis with The Science Practices

27. Non-Evolution: when evolution is NOT occurring
At the turn of the last century, when Mendel’s experiments were rediscovered, it had become clear that the genetic material was particulate in nature. Most biologists accepted evolutionary theory as described by Darwin. Evolutionary biologists, namely, R. C. Punnett, were struggling to explain why dominant alleles do not tend to increase in frequency in a population.
i.e. dominant alleles should become fixed in a population, and thus displace all recessive alleles.

28. Non-Evolution: when evolution is NOT occurring
Dominant alleles should become fixed in a population, and thus displace all recessive alleles.
Let’s test this idea with a random mating simulation.
Homework:
How do we detect a statistically significant change in allele frequencies after several generations?
What does the H-W Equilibrium Theorem predict should be the genotype frequencies after several generations? A a

29. Non-Evolution: when evolution is NOT occurring

30. Non-Evolution: when evolution is NOT occurring

31. Non-Evolution: when evolution is NOT occurring
The Hardy-Weinberg equilibrium describes the constant frequency of alleles in a gene pool.
If p and q represent the relative frequencies of the only two possible alleles in a population at a particular locus, then
p2 + 2pq + q2 = 1
where p2 and q2 represent the frequencies of the homozygous genotypes and 2pq represents the frequency of the heterozygous genotype.

32. p2 + 2pq + q2 = 1
Figure 23.7 The Hardy-Weinberg principle

33. Non-Evolution: when evolution is NOT occurring
The Hardy-Weinberg theorem describes a population that is not evolving.
This theorem states that the frequencies of alleles and genotypes in a population’s gene pool remain constant from generation to generation provided that only Mendelian segregation and recombination of alleles are at work.
In a given population where gametes contribute to the next generation randomly, allele frequencies will not change.

34. Non-Evolution: when evolution is NOT occurring
The five conditions for non-evolving populations are rarely met in nature:
Extremely large population size (therefore, no genetic drift)
No gene flow (no immigration or emigration)
No mutations
Random mating (no sexual selection)
No natural selection
The Hardy-Weinberg theorem describes a hypothetical population, but in real populations, allele and genotype frequencies do change over time.

35. What alters allele frequencies?
Three major factors alter allele frequencies and bring about most evolutionary change
Genetic drift
Gene flow
Natural selection
Natural Selection: Differential success in reproduction results in certain alleles being passed to the next generation in greater proportions.

36. Gene Pools and Allele Frequencies
When allele frequencies change in a population from one generation to the next, evolution has occurred.
Determining the mechanism(s) for allele frequency change is often complicated. What mechanism(s) is/are involved in lactase persistence and nonpersistence?
Sometimes the mechanisms are straightforward:
industrial melanism
and
natural selection.
Simulation of genetic drift of 20 unlinked alleles in populations of 10 (top) and 100 (bottom). Drift to fixation is more rapid in the smaller population.

37. How does Natural Selection Affect Allele Frequencies?
Hypothesis: Nature selects against deleterious recessive alleles.
Prediction: An allele that is under selection pressure will be driven to significantly low frequencies in a population over several generations.
How do we detect a statistically significant change in allele frequencies after several generations?
What does the H-W Equilibrium Theorem predict should be the genotype frequencies after several generations? AA Aa aa

38. How does Natural Selection Affect Allele Frequencies?
Hypothesis: Nature selects against deleterious recessive alleles.
Prediction: An allele that is under selection pressure will be driven to significantly low frequencies in a population over several generations.
How do we detect a statistically significant change in allele frequencies after several generations?
What does the H-W Equilibrium Theorem predict should be the genotype frequencies after several generations?
Langendorf and Strode (2016) Google Spreadsheet Simulation Link