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Reading “A Handbook for Biological Investigation.”

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Reading “A Handbook for Biological Investigation.”

Yes….. I expect you to read it!

Maybe more than once!

- No tests
- No quizzes
- Performance only: You must know the information and use it – like a reference book!

- What is Science?
- What are the limitations of science?
- What are the underlying assumptions of science?
- Re-read it now and then continue.

- Science is a process by which observations lead to hypothesis. (inductive logic step)
- Hypotheses are then tested through deductive logic (cause and effect)
- Rejected hypotheses make way for more research.
- Accepted hypotheses support, but DO NOT prove, ideas about how the world works.
- Science always leads to new questions, which must be tested!

- Science can only study natural phenomena.
- Science cannot make moral judgments

- All phenomena are natural
- The natural world works via cause and effect relationships
- The study of small populations allows us to generalize about larger population behavior. (this is actually one of the most controversial or the ideas.)

- Focuses on the natural world
- Aims to explain the natural world
- Uses testable ideas
- Relies on evidence
- Involves the scientific community
- Leads to ongoing research
- Benefits from scientific behavior

- Where are the things we can study?
- Broad vs. Narrow questions
- Do broad questions require simple or complex studies?
- Should we study broad or narrow questions?

Anything you are interested in can be studied if you properly develop questions into hypothesis. PASSION ASSIGNMENT

- We are encouraging you to focus on narrow questions because larger questions require more complex design and analysis.

- What are the types of measurement scales?
- Distinguish between nominal and ordinal scales.
- Distinguish between ratio and interval scales.
- Distinguish discrete data from continuous data.
- Why is it important to know what type of data you are collecting?

- Discrete Data - count data, answers questions about how many and how frequent. i.e. distributions of colors in a population of cats.
- Nominal – unranked categories.
- Ordinal- ranked categories.

- Continuous data – data measured on a scale. Answers questions about correlations, means, std. deviation, etc.
- Interval – no true zero point (i.e. arbitrary zero point) I.e. Celsius.
- Ratio – actual zero point ie: Kelvin scale

- Most Information Least Information
- Ratio scale & Interval scale: Both of these can discuss correlations between the variables
- Ordinal scale & discrete scale: These have less information & can only identify distributions within a population (how frequent or how many

- The type of data you are collecting effects what type of answers you can find.
- Must collect data that is appropriate to answer the questions you ask.
- Correlations can only be studied with measurement data
- Differences can be studied with any of these data types.

- Frequency histograms
- Mean, median and mode
- Normal distributions
- Most importantly, that distributions within populations tend to be centered around a central value.

- Why is it important to understand the spread of the data and not just the central tendency?
- How do we measure the spread and what does it tell us?
- Define variance and standard deviation.

- Variance and standard deviation are very important because they quantify the spread of the data and allow us to compare two populations. You need to understand this more so check these links that might help.
- http://www.mathsisfun.com/standard-deviation.html
- http://sciencepolicy.colorado.edu/students/envs_5120/statistics_2_6.pdf

- What is the null hypothesis?
- hypothesis that states there is no difference/ correlation between the variables

- What is the change hypothesis?
- hypothesis that states there is a difference/ correlation between the variables

- Determine your question
- Consider what type of data the question needs
- Define the variables you are studying
- Write your hypothesis
- Set up data tables
- Align data collection with statistical test

- Notice only after all of this pre-work can you even hope to write a procedure.
- Remember the procedure has only one function; to collect good data!
- You cannot write or execute a procedure without a strong understanding of what your data will look like.

- What is the difference between correlation and difference testing?
- How much difference is necessary to say two populations are different?
- What is a confidence interval?
- What is the difference between parametric and nonparametric tests?
- When do you reject the change hypothesis?
- When do you reject the null hypothesis?

- This is one of our focus topics during the class. Do your best to understand it and the first several days will include lectures on this topic.
- Cannot experiment without determining your method of data analysis
- Red flag: Here is my data. What
statistical test do I use? Then I

know you did not do slide # 17!!