Foundations of Biology !

1. Foundations of Biology!

2. AP Biology Topics UNIT 1: CHEMISTRY OF LIFE Chemistry basics, macromolecules, properties of water UNIT 2: CELL STRUCTURE/FUNCTION Cell structure, cell transport, etc UNIT 3: CELLULAR ENERGETICS Enzymes, Photosynthesis, Cell respiration, etc UNIT 4: CELL COMMUNICATION/CELL CYCLE As stated above

3. AP Biology Topics UNIT 5: HEREDITY Meiosis, Genetics, Pedigrees UNIT 6: GENE EXPRESSION DNA Structure and Replication, Protein, Epigenetics, Mutations, Biotechnology UNIT 7: EVOLUTION Natural Selection, Artificial Selection, Evidence,Speciation, Phylogeny Extinction, Origins of Life on Earth UNIT 8: ECOLOGY Energy Flow, Population Ecology, Density Effects, Biodiversity, Disruptions in Ecosystems

4. AP Test When: Beginning of May How much money: $94 total. $40 due by September (will start collecting August 12th)

5. AP Test Format I: Multiple Choice: 60 questions, counts 50% of test grade, 90 minutes long II. Free-Response: 6 questions, counts 50% of test grade, 90 minutes long • We will have progress checks (on computer) after every unit for practice

6. Science as a Process Chapter 1 – Part 2

7. Scientific Method

8. It starts with a question, then… • You make a hypothesis • Tentative explanation based on previous knowledge • Must be testable • Can be eliminated, but not confirmed with certainty • Does NOT have to be in “if then”. Often “if then” statements are predictions not hypotheses.

9. Statistical Hypothesis 1. Null Hypothesis: H0- No difference between the samples (variables) being tested. Says the observed difference between two samples is due to chance.(not due to the effect the independent variable has on the dependent variable) We use stats to try to “reject the null hypothesis” Or “fail to support the null hypothesis” Examples… Ho = There will be no statistical difference between the grades of students who listen to music while they study vs those who do not. “there will be no statistical difference between the expected data and the observed data” (chi square)

10. Statistical hypothesis 2. Alternative hypothesis: H1 or HA Opposite of the null The IV does affect the DV, “X causes Y” Goal: to “support the alternative hypothesis” by “rejecting” the null or “failing to support the null" Ex: There is a statistical difference between the grades of students who listen to music vs those who do not listen to music.

11. Does the amount of protein affect growth rate of mice? • What would a proper NULL hypothesis for this question be?

12. Experimental Design • Large sample size • Replicated many times (many trials) • Control Group • The “baseline”…what results compared against • Not present in “comparative” investigations • Experimental Groups- all other groups that are tested • Controlled Variables (Constants) • Remain the same between all groups, so that they are NOT factors in the experiment

13. Experimental Design - Variables Independent Variable (“I” set up beforehand) ~ is the only variable that is changed between experimental groups ~ example: color of light on plants Dependent Variable (“Data” collected “During” experiment) ~ is the effect of the independent variable ~ it is what you measure as you experiment ~ ex: height plants grow

14. Does the amount of protein affect growth rate of mice? • What would the control for this experiment be? • What are the… • IV • DV • CV

15. Experimental Design • Bob wants to see which fertilizer will make his tomato plants produce the largest # of tomatoes. He uses the same variety of tomato, same amt & type of soil, same amt of water & plants them in same area. • Group 1 gets no fertilizer, group 2 gets Brand Q, group 3 gets Brand R, group 4 gets Brand S. • Identify: • IV • DV • CV • control group • experimental groups

16. Data Tables- IV on left, DV on right

17. Data Tables Table 1. Height of Sunflowers when Grown in Varying Colors of Light

18. Experimental Design • Sue wants to see if plant food makes rose bushes produce more flowers. • Identify: • IV • DV • CVs • Control group • Null hypothesis • On her data table, what would the labels be at the top? (which one goes where?) • What would the labels on each axis of a graph be?

19. Analyzing Data Standard Deviation How spread out is the data? Standard Error of the Mean How likely is it that our “mean” is a good value? Chi-square test & t-Test How close to our “expected” is close enough?

20. Analyzing Data Standard Deviation: How spread out is the data? how far on average any data point is from the mean the smaller the SD, the closer the scores are to mean when SD is large, the scores are more widely spread

21. Analyzing Data Standard Deviation: Moving a SD away from mean in either direction lets us estimate what % of points are within that section In science we almost always go with the 95% confidence level… this is equal to +/-2 SD units

22. Analyzing Data Standard Deviation: To calculate, you need to know… Individual data points (x) Mean of data points ( ) Sample size (n)

23. Analyzing Data Standard Deviation: Steps… 1. Calculate mean 2. Fill out chart with calculations (subtract & square) 3. Add up last column 4. Divide that sum by (n-1) 5. Take square root of that number

24. Analyzing Data Standard Error of the Mean How likely is it that our “mean” is a good value? the higher the sample size, the more sure we can be to calculate this, simple divide the standard deviation value (s) by the square-root of the sample size. Yes, you have already done most of the work by now! In science we almost always use 2xSEM to get to the 95% confidence level

25. Graph – DRYMIX Graph 1. Height of Sunflowers when Grown in Different Colors of Light Height of Sunflowers (cm) Color of Light

26. Which Graph to Use? Check the x-axis (the IV) If it is numbers…make it a LINE graph If it is words…make it a BAR graph

27. Which Graph to Use? Bar Graph For discrete data…words on the X!

28. Which Graph to Use? Modified Bar Graph Includes error bars (see SEM)

29. Which Graph to Use? Line Graph/Scatter-Plot Line of best fit (regression line) For continuous data…#s on the X!

30. Analyzing Data Standard Deviation How spread out is the data? Standard Error of the Mean How likely is it that our “mean” is a good value? Chi-square test & t-Test How close to our “expected” is close enough?

31. Analyzing Data Standard Deviation: How spread out is the data? how far on average any data point is from the mean the smaller the SD, the closer the scores are to mean when SD is large, the scores are more widely spread

32. Analyzing Data Standard Error of the Mean How likely is it that our “mean” is a good value? the higher the sample size, the more sure we can be to calculate this, simple divide the standard deviation value (s) by the square-root of the sample size. Yes, you have already done most of the work by now!

33. Fake Quiz! Answer on scratch paper (or in your notes). Don’t look at your notes! Answer everything!

34. Bob wants to see if having earthworms in the soil will make his pepper plants produce more peppers. He sets up 4 planting pots with soil, pepper plant seedlings, & water. To the 4 pots he adds 0, 2, 4, and 6 earthworms. He allows the plants to grow for 2 months, counting the total # of peppers produced during that time. 1. What is the IV? 2. What is the DV? 3. List any 3 controlled variables that will be needed. 4. Which is the control group? 5. What is the null hypothesis for this experiment?

35. Free Response – an example… A student wondered whether radish seeds require a certain number of hours of light in order to sprout successfully. • Identify the IV and DV. Describe measures taken to allow for controlled variables. • Write a null hypothesis for this experiment. Write an alternate hypothesis for this experiment. (Label each.)

36. Free Response – an example… A student wondered whether radish seeds require a certain number of hours of light in order to sprout successfully. • Design a controlled experiment to test the null hypothesis. Describe data that would cause you to reject your null hypothesis. Defend your answer.