Teaching with Tech

Statistical Analysis

Identify outliers in your raw data, then run the correct significance test and generate a written interpretation for your IA.

1 Outlier Detection
2 Significance Test
IV Type
Continuous
Categorical
Variables
Independent Variable (x-axis)
Dependent Variable (y-axis)
Raw Trial Data
Appearance
Box Fill Colours
Box Fill Style
📊

Enter your raw trial data to see box-and-whisker plots for each group

Which Statistical Test?
Click each bubble to trace a path through the decision tree. Click a selected bubble again to deselect it. When you reach a test at the bottom, the Run Test button will activate.
What type of data is your DV? Continuous / Measured You measured a numerical value with an instrument. e.g. mass in grams, time in seconds, length in cm Categorical / Count You counted how many things fell into each category. e.g. number of Daphnia choosing red / blue / green Ranked / Ordinal Data placed in order, not measured exactly. e.g. rating plant health as 1 (poor) to 5 (excellent) Chi-Squared (χ²) Spearman's RankTests if rankings follow a trend Significant Difference? Are the means of your groups genuinely different? Works for any pattern — including peaks e.g. enzyme rate vs temperature where rate rises then falls Correlation? Does the DV consistently go up (or down) in a straight line as IV increases? e.g. light intensity vs photosynthesis rate in Elodea ⚠ Not for data that peaks or curves Pearson's rTests strength of linear trend 2 groups e.g. control vs treatment 3+ groups e.g. 4 different pH levels t-test ANOVA+ Tukey HSD KEY Your selected path Available choices Greyed out = not applicable
© B. Moran, 2026