Concepts to cover when using Data Nuggets in the classroom
What is a Hypothesis?
- Strode (2015) Hypothesis Generation in Biology: A Science Teaching Challenge & Potential Solution. The American Biology Teacher, 77(7): 500-506.
- The difference between a prediction and hypothesis – Paul Strode
- Writing a scientific hypothesis (teacher guide) (student guide) – Paul Strode
- Hypotheses and theories explained – Understanding Science, UC Berkley
- Hypothesis misconceptions – Understanding Science, UC Berkley
- Hypotheses vs. predictions – blog post from Psychology Today
- Scientific vs. Statistical Hypotheses
The Process of Science
- What is science?
- Science as a process (flowchart)
- Understanding Science’s teacher resources
- FAQ about how science works
- What is Science? video – Science Forward
- Observational vs. Hypothesis Driven Studies
Reading Scientific Texts
- Data Nugget Guide to Reading a Scientific Paper
- Content area reading strategies – Journey North
- How to read a scientific article – Rice University
Constructing Explanations using Claim-Evidence-Reasoning (CER)
- Using the CER Framework – Conversation between Dr. Kate McNeill and Dr. Joe Krajcik, authors of the book on CER
- Data Nugget CER Scaffolding Tool for Constructing Explanations
- BSCS Scientific Explanation Scaffolding Tool and Student Guide
- American Museum of Natural History CER Scaffolding Tool
- Data Nugget Guide to Graphing
- Graph Choice Chart (GCC) – a tool to help students turn data into evidence
- AP Biology Quantitative Skills – a guide for teachers
- The Fundamentals of Data Visualization – a guide to making visualizations that accurately reflect the data, tell a story, and look professional. Grown out of experience working with students and postdocs in my laboratory on thousands of data visualizations.
- Data Points – HHMI – interpret and discuss figures from primary literature
- HHMI Teacher Guide: Math and Statistics – Topics include measures of average (mean, median, mode), variability (range, standard deviation), uncertainty (standard error, 95% confidence interval), Chi-square analysis, student t-test, Hardy-Weinberg equation, frequency calculations, and more.
- Data and Error Analysis in Science – Beginners Guide (mean, median, mode, range, standard deviation, variance, standard error, and confidence intervals) – Strode and Brokaw
- Teaching Statistics: Going from Scary, Boring, and Useless to, Well, Something Better
- EDDIE Statistical Vignettes – focused on developing quantitative concepts commonly used in the analysis of data, and intended to help students address statistical misconceptions and improve their quantitative reasoning skills.
- MathBench – These modules introduce students (and anyone else who’s interested) to the mathematical underpinnings of what they learn in introductory biology courses. But unlike a textbook, the modules are not full of equations and proofs. Instead, we try to bring math to life using intuitive approaches, everyday situations, and even humor.
- Error Bars – Interpretation of SD, SE, and CI
- Teaching independent vs. dependent variables
- ANOVA – Student Guide – Paul Strode
- Regression Analysis – Student Guide – Paul Strode
- Chi-square Test – Student Guide – Paul Strode
- t-test – Student Guide – Paul Strode
- Mean, standard deviation (SD), standard error (SE)
- Lesson plan
- Worksheets: (red) (blue)
- Standard error (SE) visualization tool – explore the effects of variance and sample size on SE
- Variable types, t-test, regression
- Lesson plan
- Case studies: (A) (B) (C)
- Flowchart for statistics used in biology – Paul Strode
- Statistics for Biologists – nature.com
- How do you know? A podcast exploring the numbers behind our beliefs, and everything in between.
- Data Literacy Project
- Youcubed – Data literacy resources for K-12
- Using Data in the Classroom – information or background about pedagogical or practical issues in using data in the classroom
- DataONE –
- Data Management
- Data Stories – success stories and cautionary tales from researchers related to their experiences with managing and sharing scientific research data
- Keeping a paper trail: basic data management skills for reproducible science