Review an experimental design to determine possible sources of bias or error, state alternative explanations, and identify questions for further investigation 0607.Inq.4

Links verified 5/17/2024

1. Bias and Random Error - bias can be large enough to invalidate any conclusions
2. Bias and Sources of Error - Learn how scientists identify and minimize bias and sources of error to produce accurate results
3. Biased Sample - also known as: Biased Statistics, Loaded Sample, Prejudiced Statistics, Prejudiced Sample, Loaded Statistics, Biased Induction, Biased Generalization (3 good examples at the bottom of the page)
4. Data Analysis and Interpretation - Did you know that scientists don't always agree on what data mean? Different scientists can look at the same set of data and come up with different explanations for it, and disagreement among scientists doesn't point to bad science.
5. Experimenter's Bias - Wikipedia entry
6. How Science works: Errors - Explains random and zero errors, parallax, and anomalous results.
7. How Science works: Reliable evidence and Valid evidence - Explains the meaning of 'data' and 'evidence', 'reliability' and 'validity', 'secondary' evidence, with examples for you to discuss.
8. Practices of Science: Scientific Error - teaching science as inquiry
9. Research Bias - a process where the scientists performing the research influence the results, in order to portray a certain outcome
10. Sampling Error - the deviation of the selected sample from the true characteristics, traits, behaviors, qualities or figures of the entire population
11. Sources of Error - [37 page .pdf document] a framework for identifying potential sources and impact of distortion in observational studies, with approaches to maintaining validity
12. Statistics with Crayons - (4:16) a YouTube video about bias and random eror with Hans and Hera
13. Systematic Error - a type of error that deviates by a fixed amount from the true value of measurement

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