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Quantitative Data

This guide reflects on key concepts in quantitative data analysis for biology scholars, emphasizing research design principles such as the distinction between experimental and correlational studies. It covers essential topics like selecting independent and dependent variables, choosing appropriate designs, and the significance of statistical tests. Readers will gain insights into descriptive and inferential statistics, the importance of p-values, and the interpretation of confidence intervals. Overall, it serves as a comprehensive resource for understanding how to analyze relationships in biological research effectively.

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Quantitative Data

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  1. Quantitative Data Biology Scholars – 2010 Reflections from a Developmental Biologist

  2. Research Design • Experimental vs. Correlational • Selecting independent and dependent variables • Choosing a design • Selecting the appropriate statistical test

  3. Statistics Descriptive Inferential Sample /Population Does the relationship in the sample exist in the population? Means Relationships Categorical Data • Mean • Median • Mode • Frequencies

  4. Captures qualities or characteristics about the individual, such as a person's eye color, gender, political party, or opinion on some issue. Categorical Data? Surveys or Experiments

  5. Significance? p-values • Any difference is due to either • Chance • Treatment effect • Measures how likely any difference is due to chance • 95% confidence interval • < or > 0.05 • Difference between means • Variability • Sample size

  6. Statistical Decision Tree

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