IBBiologyInternal AssessmentHLSL

Biology IA Statistical Analysis Guide

Choose and apply the correct statistical tests for your IB Biology IA, including t-tests, chi-squared, and correlation analysis.

Biology IAStatisticsData ProcessingStatistical TestsInternal Assessment
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Act as an IB Biology IA examiner specializing in data analysis. Help me process data and choose statistical tests for my Biology IA: **CHOOSING THE RIGHT STATISTICAL TEST:** 1. **t-test (comparing two means)**: - Use when: Comparing two groups (e.g., enzyme rate at pH 5 vs pH 7) - Requirements: Normal distribution, continuous data, two groups only - Calculate: $$t = \frac{\bar{x}_1 - \bar{x}_2}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$$ - Compare t-calculated to t-critical at p = 0.05 - If $t_{calc} > t_{crit}$: reject null hypothesis (significant difference) 2. **Chi-squared test ($\chi^2$) (comparing observed vs expected)**: - Use when: Categorical data (e.g., phenotype ratios in genetics) - Calculate: $$\chi^2 = \sum \frac{(O - E)^2}{E}$$ - Compare to critical value at appropriate degrees of freedom - If $\chi^2_{calc} > \chi^2_{crit}$: reject null hypothesis 3. **Correlation coefficient (r)**: - Use when: Testing relationship between two continuous variables - Pearson's r: ranges from -1 to +1 - $|r| > 0.7$: strong correlation; $0.4 < |r| < 0.7$: moderate; $|r| < 0.4$: weak - **Correlation does not imply causation!** **DATA PRESENTATION:** 4. **Tables**: Raw data with units, uncertainties, and meaningful headers 5. **Graphs**: - Scatter plots for continuous IV vs DV (with error bars and line of best fit) - Bar charts for categorical comparisons (with error bars showing SD or SEM) - Include axis labels, units, and titles 6. **Descriptive Statistics**: - Mean ($\bar{x}$), standard deviation ($s$), range - Standard error of the mean: $$SEM = \frac{s}{\sqrt{n}}$$ 7. **Interpreting Results**: - State null hypothesis clearly - Report the test statistic and p-value - Decide whether to accept or reject the null hypothesis - Explain what this means IN BIOLOGICAL TERMS **Common Mistakes:** - Using the wrong statistical test for your data type - Not stating the null hypothesis before testing - Interpreting correlation as causation - Forgetting to include error bars on graphs - Not explaining what the statistical result means biologically **IB Tip:** You don't need to use ALL tests — choose the one most appropriate for YOUR data. Quality over quantity. **My data and research question:** [DESCRIBE YOUR DATA AND WHAT YOU WANT TO ANALYZE]

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