IB Data Table and Analysis Tool

LAYOUT:
DECIMALS:

Comparative Statistical Analysis

Validation Note

This calculator seamlessly isolates Raw Trial Data from either layout mode, matching Excel's exact statistical output.

IB Statistics Glossary

  • SD (Standard Deviation): Measures the amount of variation or dispersion in your data. A low SD indicates data is tightly clustered around the mean.
  • SEM (Standard Error of the Mean): Estimates how far your sample mean is likely to be from the true population mean. It accounts for sample size.
  • p-value: The probability that your results happened by random chance. A value p < 0.05 is generally considered statistically significant.
  • Levene's Test: Assesses the equality of variances across your groups. If p > 0.05, your data's variance is homogenous (equal spread), which guides the calculator to pick the most accurate test type.
  • T-Test: A statistical test used to compare the means of exactly two distinct groups.
    • Student's T-Test: The classic version, used when variances are equal.
    • Welch's T-Test: The robust alternative, used automatically when variances are unequal.
  • ANOVA (Analysis of Variance): A "Single-Factor" (or One-Way) test used instead of multiple T-Tests to compare three or more groups simultaneously across one independent variable.
    • Fisher's ANOVA: The classic version, used when variances are equal.
    • Welch's ANOVA: The robust alternative, used when variances are unequal.
  • Post-Hoc Test: A follow-up test conducted only if an ANOVA finds a significant difference, used to determine exactly which specific groups differ from each other.
  • Bonferroni Correction: A strict mathematical penalty applied during Post-Hoc testing to prevent false positives (Type I errors) when making multiple pairwise comparisons.

💡 Stats Tip: The Post-Hoc Paradox

Can an ANOVA be significant, but the Post-Hoc test finds no differences? Yes! ANOVA looks at the "big picture" of all groups combined, giving it the power to spot subtle overall trends. Post-Hoc tests (like Bonferroni) look at groups locally, two at a time, and apply a massive penalty to prevent false positives. If your ANOVA is only barely significant (e.g., p = 0.04), that heavy penalty can wipe out the significance of individual pairs!

Citable Sources for Your Lab Report:

  • Handbook of Biological Statistics (Univ. of Delaware): Plain-English guides for biology students. (T-Tests) | (ANOVA)
  • GraphPad Statistics Guide: Industry-standard, intuitive biostatistics. (SD vs SEM) | (Bonferroni)
  • OpenStax Intro to Statistics (Rice Univ.): Free, peer-reviewed textbook. (Assumptions & Logic)