Statistical Parametric Mapping (SPM) is a method used to analyze continuous curves at each time point. Instead of applying a standardized statistical test at a single time point, SPM applies the test at every time point along a waveform. This process creates a statistical “map” that lives in the same dimension space as your original data, making the results intuitive to interpret.

SPM in Sift supports:

• One Sample T-Test
• Two Sample T-Test
• Paired T-Test
• One-Way ANOVA

Visualization of a two-sample t-test applied across a continuous curve

Traditional statistics, such as t-tests and ANOVAs, are effective for comparing group means, but they have major limitations when applied to continuous data: it is often unclear at which specific point the test should be applied (e.g., the maximum value, 50% through the gait cycle, or another moment in time), and all information outside of that moment is lost.

SPM solves this limitation by evaluating the entire curve. The key benefits include:

• No Discarded Data: The analysis is applied across the entire curve, so there is no loss of information outside a single point.
• Eliminates Bias: It removes the guesswork and bias involved in manually selecting peak values or averages.
• Deeper Insights: It provides continuous statistical inference, allowing you to see exactly when and where a significant difference occurs during a motion.

More detail on the mathematics behind SPM can be found on the page: The Math of Statistical Parametric Mapping

To see the range variation while the timing of the event is irrelevant, make sure to apply curve registration to your signal prior to running SPM.

Sift provides a number of ways to visualize and interact with the results of SPM. An overview of all SPM visualizations is available on the Sift - Analyse Page

This page includes:

• Visualizing the maps;
• Visualizing the statistics;

Choosing your experimental hypothesis is very important, and this should influence the statistical test being undertaken. ANOVA provides us a broad look at all of our data: with the hypothesis that all groups have the same mean, we can easily test IF there is 1 or more groups not following this hypothesis, but we cannot discern which one it is. T-tests on the other hand can specifically tell us if any 2 groups are different, and specifically identify which tests are different.

For many groups, it is recommended to first use an ANOVA test, and if there is statistical differences, to use post-hoc t-tests with a bonferroni correction (or the Holm–Bonferroni method) to identify which group this is.

For related groups, it is recommended to use a paired t-test over a two-sample t-test, as it has strictly higher statistical power.

Step-by-step tutorials are available for SPM in Sift:

• SPM Tutorial.
• Analysis of Shoulder Angular Velocity between Elite Level and Average Collegiate Pitchers
• Effects of Fatigue During Basketball Free Throws Using SPM and ANOVA
• Impact of Shooting Arm Mechanics on Free Throw Accuracy Using PCA, K-Means, and SPM

Our implementation of Statistical Parametric Mapping is based on articles by Todd Pataky, as well as the textbook on the topic: “Statistical Parametric Mapping - The Analysis of Functional Brain Images”:

K. Friston, J. Ashburner, S. Kiebel, T. Nichols, and W. Penny, Statistical Parametric Mapping: The Analysis of Functional Brain Images, 1st ed.; Academic Press: Cambridge, MA, USA, 2006; ISBN 978-0-08-046650-7.

Pataky TC (2010) Generalized n-dimensional biomechanical field analysis using statistical parametric mapping. Journal of Biomechanics 43. 1976-82 ([1])