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Understanding and Identifying Data Bias and Experimental Errors in Research

This resource focuses on identifying faulty interpretations of data caused by bias and experimental errors. It outlines key concepts, including definitions of bias and experimental error, and explains how these can influence research outcomes. The document provides a structured approach with varying levels of complexity, from defining key vocabulary and sources of bias to recommending methods for controlling bias, such as double-blind testing and independent analysis. Aimed at enhancing critical thinking skills in data interpretation, this guide is essential for researchers.

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Understanding and Identifying Data Bias and Experimental Errors in Research

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  1. 804 Objective 804: Identify a faulty interpretation of data due to bias or experimental error.

  2. Identify a faulty interpretation of data due to bias or experimental error. Key concepts/skills: Level 1: Define key vocabulary. List possible sources of bias or error. Level 2: Given a description of experiment or data collection, identify the possible source of bias or error. Level 3: Given a description of experiment or data collection describe methods or procedures to eliminate bias or error.

  3. Identify a faulty interpretation of data due to bias or experimental error. Key vocabulary: bias experimental error (review conclusion)

  4. Identify a faulty interpretation of data due to bias or experimental error. I. Bias A. What is bias 1. A wish or expectation that an experiment will lead to a certain conclusion. 2. Example: If a pharmaceutical company is testing its own drug, the company and its employees want the drug to work so they can market it and make money.

  5. Identify a faulty interpretation of data due to bias or experimental error.

  6. Identify a faulty interpretation of data due to bias or experimental error. II. Guarding against Experimental errors A. Methods 1. Measure more than once 2. Review data 3. repeat experiment

  7. Identify a faulty interpretation of data due to bias or experimental error. III. Guarding against Bias A. Methods 1. double-blind testing- researchers nor subjects know who gets what treatment 2. placebo- artificial treatment 3. Independent researcher- researchers who do not have an interest in the success or failure of a product or idea

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