1. Finding Journal Articles
Google Scholar (how it works)
Curated databases:
Science Direct, Web of Knowledge/Science, SCOPUS, etc.
Off-Campus Access:
UCSBnetID: same as Gold, GauchoSpace, and U-Mail
Can also click the “Services” tab ->> Off-campus access for more information

2. Off-Campus Access

3. Interpreting Evidence
Inspired by UCSB Faculty Professor Dr. Kathy Foltz
“I spent a year studying philosophy in the mountains.” - This is true, but it’s deceiving, because it’s not the whole story. My college just happened to be in the mountains and I took two semesters of philosophy. Sometimes it’s hard to interpret the evidence.
Elijah J. Spina

4. What does this mean? Interpreting Evidence
Sometimes it’s hard to interpret the evidence.

5. Important Questions What is scientific evidence?
Interpreting Evidence
Important Questions
What is scientific evidence?
How is evidence generated?
How can evidence be used to test hypotheses?
- All three are intertwined, but the main parts of each are described separately then built up throughout the presentation.
- In order to answer these questions, we should review a little about the philosophy of science and break down the “nature of science” while we revisit some basic aspects of how science works on the largest scale.
- Once we reach the third question (interpreting evidence), we’ll break for an activity before quickly discussing statistics at the end.

6. Important Questions What is scientific evidence?
Interpreting Evidence
Important Questions
What is scientific evidence?
How is evidence generated?
How can evidence be used to test hypotheses?
- All three are intertwined, but the main parts of each are described separately then built up throughout the presentation.
- In order to answer these questions, we should review a little about the philosophy of science and break down the “nature of science” while we revisit some basic aspects of how science works on the largest scale.
- Once we reach the third question (interpreting evidence), we’ll break for an activity before quickly discussing statistics at the end.

7. Interpreting Evidence
What is science?
The systematic observation of natural events and conditions in order to discover facts about them and to formulate laws and principles based on these facts. The organized body of knowledge that is derived from such observations and that can be verified or tested by further investigation. Academic Press Dictionary of Science & Technology Science consists simply of the formulation and testing of hypotheses based on observational evidence; experiments are important where applicable, but their function is merely to simplify observation by imposing controlled conditions. Robert H. Dott, Jr., and Henry L. Batten, Evolution of the Earth Science alone of all the subjects contains within itself the lesson of the danger of belief in the infallibility of the greatest teachers in the preceding generation. As a matter of fact, I can also define science another way: Science is the belief in the ignorance of experts. Richard Feynman

8. Terms Describing the “Nature of Science”
Interpreting Evidence
Terms Describing the “Nature of Science”
Fact: An observation that has been repeatedly confirmed and for all practical purposes is accepted as "true." Truth in science, however, is never final, and what is accepted as a fact today may be modified or even discarded tomorrow.
Hypothesis: A falsifiable statement about the natural world.
Law: A descriptive generalization about how some aspect of the natural world behaves under stated circumstances.
Theory: A well-substantiated explanation of some aspect of the natural world that can incorporate facts, laws, inferences, and tested hypotheses.
Modified from National Academy of Sciences, 1999
Fact: Can inferences be considered facts if not directly observed?
Hypothesis: If the deductions are verified, it becomes more probable that the hypothesis is correct. If the deductions are incorrect, the original hypothesis can be abandoned or modified. Hypotheses can be used to build more complex inferences and explanations.

9. Interpreting Evidence
Scientific Theories
The contention that evolution should be taught as a "theory, not as a fact" confuses the common use of these words with the scientific use. In science, theories do not turn into facts through the accumulation of evidence. Rather, theories are the end points of science. They are understandings that develop from extensive observation, experimentation, and creative reflection. They incorporate a large body of scientific facts, laws, tested hypotheses, and logical inferences. In this sense, evolution is one of the strongest and most useful scientific theories we have.

10. Scientific Theories Theory of General Relativity Cell Theory
Interpreting Evidence
Scientific Theories
A central prediction from a current theory: the general theory of relativity predicts the bending of light in a gravitational field. This prediction was first tested during the solar eclipse of May 1919.[6]
The first observation of cells, by Robert Hooke, using an early microscope.[8] This led to the development of cell theory: 1) All living organisms are composed of one or more cells, 2) The cell is the most basic unit of life, 3) All cells arise from pre-existing living cells.
Theory of General Relativity
Cell Theory

11. Interpreting Evidence
Paradigms
Thomas Kuhn ( ) The Structure of Scientific Revolutions “Universally recognized scientific achievements that, for a time, provide model problems and solutions for a community of practitioners.” Wikipedia: The set of practices that define a scientific discipline at any particular period of time.
Kuhn argued science operates under one paradigm for a while, then a revolution or “paradigm shift” occurs during a short time.

12. Evidence of Causality Necessity (lose it): Sufficiency (move-it):
Interpreting Evidence
Evidence of Causality
Necessity (lose it):
If “X” is a necessary cause of “Y”
Then the presence of “Y” implies the presence of “X”
However, the presence of “X” does not imply “Y” will occur.
Sufficiency (move-it):
If “X” is a sufficient cause of “Y”
Then the presence of “X” implies the presence of “Y”
However, something else may alternatively cause “Y”
Thus, the presence of “Y” does not imply the presence of “X”.
Correlation is indirect evidence of causality! It is primarily limited to inference.
Necessity & sufficiency are restricted to the domain of deductive reasoning in philosophy! Then how can we use them in experiments that rely on inductive reasoning?

13. Types of Reasoning Inductive logic: “bottom up”
Interpreting Evidence
Types of Reasoning
Inductive logic: “bottom up”
Inferring a general conclusion
from individual observations
Deductive logic: “top down”
Narrowing down general cases
to a specific conclusion
The problem of induction (Popper – if we never rule things out, how can we ever be sure) and
Models of explanation (Hempel’s Deductive-nomological & Inductive-statistical models).

14. Important Questions What is scientific evidence?
Interpreting Evidence
Important Questions
What is scientific evidence?
How is evidence generated?
How can evidence be used to test hypotheses?
- All three are intertwined, but the main parts of each are described separately then built up throughout the presentation.
- In order to answer these questions, we should review a little about the philosophy of science and break down the “nature of science” while we revisit some basic aspects of how science works on the largest scale.
- Once we reach the third question (interpreting evidence), we’ll break for an activity before quickly discussing statistics at the end.

15. The Scientific Method Hypothesis Prediction Experiment Interpret
Interpreting Evidence
The Scientific Method
Hypothesis
Prediction
Experiment
Interpret
Make predictions
using hypothesis
Design experiment
(test predictions)
Collect observations
What’s unexplained?
This is usually how “scientific” evidence is generated, but varies depending on your definition.

16. Reductionism The whole is just the sum of the parts.
Interpreting Evidence
Reductionism
The whole is just the sum of the parts.
“Reductionism exposes the nature of a system, whereas scientific hypothesis testing allows for the synthesis of scientific truth.” - George Hart (LSU)
Reductionism vs. Holistic Science: A False Dichotomy?
Large scale organization can influence smaller scales
Systems may be defined by dynamics instead of components (e.g. self-assembly)
Feedback loops create structure at a given level, independently of details at a lower level of organization.
This is the first half of the link between inductive and deductive reasoning in science. Generalizations are supported through individual observations in the context of reductionist hypotheses (inductive reasoning) and counter-arguments called experimental controls (deductive reasoning), eventually leaving only one possible conclusion.
This can also be considered abductive logic, or inference to the best explanation, unless all possible alternates can confidently be ruled out.

17. Interpreting Evidence
Controls
An experiment where the variable being tested is left unchanged.
The “answer” should already be known.
Represent the null hypothesis.
Nothing is being tested. It’s either going to show the effect (positive control) or have no effect (negative control).
This is the second half of the link between inductive and deductive reasoning in science. Generalizations are supported through individual observations (inductive reasoning) and counter-arguments called experimental controls (deductive reasoning), eventually leaving only one possible conclusion.
This can also be considered abductive logic, or inference to the best explanation, unless all possible alternates can confidently be ruled out.

18. Interpreting Evidence
Activity
Discuss the examples on the handout Think about types of reasoning and causality Preferably work in pairs or groups of 3

19. Important Questions What is scientific evidence?
Interpreting Evidence
Important Questions
What is scientific evidence?
How is evidence generated?
How can evidence be used to test hypotheses?
- All three are intertwined, but the main parts of each are described separately then built up throughout the presentation.
- In order to answer these questions, we should review a little about the philosophy of science and break down the “nature of science” while we revisit some basic aspects of how science works on the largest scale.
- Once we reach the third question (interpreting evidence), we’ll break for an activity before quickly discussing statistics at the end.

20. Statistical Inference
Interpreting Evidence
Statistical Inference
The process of drawing conclusions from data that are subject to random variation.
Strengthens inductive arguments
Paradigms:
Frequentist: sharp decision rules for propositions
Bayesian: degrees of belief in a proposition
Frequentists use p-values and confidence intervales
Bayesians use prior/posterior probabilities

21. Interpreting Evidence

22. Statistical vs. Practical Significance
Interpreting Evidence
Statistical vs. Practical Significance
Given a sufficiently large sample size, a non-null statistical comparison will always show a statistically significant results unless the population effect size is exactly zero (and even there it will be shown to be statistically significant at the rate of the Type I error – false positives – used). For example, a sample Pearson correlation coefficient of 0.01 is statistically significant if the sample size is Reporting only the significant p-value from this analysis could be misleading if a correlation of 0.01 is too small to be of interest in a particular application.
Nature, 506, 150–152 (13 February 2014)

23. Illustrating A “Long Shot”
Interpreting Evidence
Illustrating A “Long Shot”

24. www.xkcd.com Interpreting Evidence
Need to adjust p-values for multiple comparisons (multiple hypothesis testing)
Illustrates how prior knowledge and experimental design are important for interpreting evidence.

25. www.xkcd.com Interpreting Evidence
The use of “confidence” and “chance of coincidence” are often misused, as illustrated here. Statistical tests should not be interpreted as defining absolute parameters of a population, in fact they are just estimates about a sample. 95% confidence suggests the mean (average) value for your experiment is 95% likely to be within a specified range (the confidence interval) of the true population mean, BUT A SIGNIFICANT TEST DOES NOT GUARANTEE THIS!!!

26. Common Types of Effect Size
Interpreting Evidence
Common Types of Effect Size
Eta-squared for multivariate
J Grad Med Educ. Sep 2012; 4(3): 279–282.

27. Error Bars JCB, Vol. 177, No. 1, April 9, 2007 7–11
Interpreting Evidence
Error Bars
Descriptive vs. inferential error bars:
JCB, Vol. 177, No. 1, April 9, –11

28. The Overlap Rule JCB, Vol. 177, No. 1, April 9, 2007 7–11
Interpreting Evidence
The Overlap Rule
Figure 5. Estimating statistical significance using the overlap rule for SE bars. Here, SE bars are shown on two separate means, for control results C and experimental results E, when n is 3 (left) or n is 10 or more (right). “Gap” refers to the number of error bar arms that would fi t between the bottom of the error bars on the controls and the top of the bars on the experimental results; i.e., a gap of 2 means the
distance between the C and E error bars is equal to twice the average of the SEs for the two samples. When n = 3, and double the length of the SE error bars just touch (i.e., the gap is 2 SEs), P is 0.05 (we don’t recommend using error bars where n = 3 or some other very small value, but we include rules to help the reader interpret such fi gures, which are common in experimental biology).
Figure 6. Estimating statistical signifi cance using the overlap rule for 95% CI bars. Here, 95% CI bars are shown on two separate means, for control results C and experimental results E, when n is 3 (left) or n is 10 or more (right). “Overlap” refers to the fraction of the average CI error bar arm, i.e., the average of the control (C) and experimental (E) arms. When n ≥ 10, if CI error bars overlap by half
the average arm length, P ≈ If the tips of the error bars just touch, P ≈ 0.01.
JCB, Vol. 177, No. 1, April 9, –11

29. Try out the overlap rule
Interpreting Evidence
Try out the overlap rule
Means w/ SE bars
n = 3
Is there significant difference between groups?
Figure 7. Inferences between and within groups. Means and SE bars are shown for an experiment where the number of cells in three independent clonal experimental cell cultures (E) and three independent clonal control cell cultures (C) was measured over time. Error bars can be used to assess differences between groups at the same time point, for example by using an overlap rule to estimate P for E1 vs. C1, or E3 vs. C3; but the error bars shown here cannot be used to assess within group comparisons, for example the change from E1 to E2.
JCB, Vol. 177, No. 1, April 9, –11

30. Important Questions What is scientific evidence?
Interpreting Evidence
Important Questions
What is scientific evidence?
It depends on context:
Questions being asked (and ability to answer them)
How observations are interpreted
How is evidence generated?
Understanding the “nature of science”
Applying the scientific method
How can evidence be used?
Building support for hypotheses (induction)
Ruling out hypotheses (deduction)
Statistical inference can help interpretation

31. Feedback Form Resources http://bit.ly/1ISIPnH PDF Downloads
Interpreting Evidence
Resources
Feedback Form
PDF Downloads
Bill Smith’s UCSB or
Eli’s Web Page
ejsbio.weebly.com