Data Analysis

Perform Lit Search for how data is normally represented

Is the Raw Data Normally Distributed?

No

Yes

Is this how the data should be represented?

Yes

No

Use non parametric plot

FIRST OPTION: Reorganize the Data

Find the Source of Variation (Scatterplots and Histograms)

If data is skewed

If Outliers are found

Can/Should outliers be removed?

Yes, there are enough subjects in the study for the power analysis

No

Were outliers different in any way NOT related to the study?

Normalize data to each subject

Test again for outliers

Explain cause of variation in paper. Remove outlier. Restart

Is there reasonable way to group subjects to explain variation?

e.g. Weight

e.g. Sex

Find a relevant ratio for a linear transformation

Test again for normality

Go to statistical analysis

SECOND OPTION: Transform the Data

Ways to Look at and Interpret Data

Group Subjects by:

Sex

Treatment

Timepoint

In each group, do subjects show a change over time?

Do males and females perform differently either befofe and/or after treatment?

Perform any common normalizations or transformation according to lit search

For proportions or Percentages

When looking for a change from normalcy, not a specific increase or decrease

For skewed data

Negative Skew

Positive Skew with non-negative values

Logarithmic transformation (for cumulative, multiplicative effects or exponential data.

Exponential transformation (for log trends e.g. decay)

Power transformation (for power components e.g. area, volume)

Arcsin transformation

Remove and restart

Square Root transformation (for counts or data with a power, e.g. area, volume)

z-score transformation

How does recovery vary across time based on treatment? Were there any differences between groups before treatment?

Statistical Analysis

Group difference

Time/Mixed effect

Distribution difference

Prediction

Sample <10 and/or Normality fail

YES - Non parametric test

NO - Parametric and Non-parametric

Chi-square test - normality

Levene's test - variance

ranksum - two sample

signrank - paired test

Kruskal-Wallis - N-sample

t-test - two-sample

paired t-test - paired test

ANOVA - N-sample

Kolmogorov-smirnov test - normality

rmANOVA - Nsample/repeated

Chi-square

Exact Fisher Test

Linear Classifier (LDA)

Support-Vector Machine

k-Nearest-Neighbour

Artificial Neural Network

Data Reduction (Dimension reduction)

PCA

LDA

ICA

multicolinearity

Backward feature elimination

Decision tree/random forest

Multiway array decomposition

Plot Data for exploration

Non parametric plot

Box-whisker plot - group spread/outliers

Scatter plot - data spread/individual

Histogram - Distribution

Parametric plot

Mean + S.E.M. - group difference/spread

click to edit

Use parametric plot + scatter plots

Explore data transformation