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Data Analysis (Statistical Analysis (Data Reduction (Dimension reduction),…
Data Analysis
Statistical Analysis
Group difference
Sample <10 and/or Normality fail
YES - Non parametric test
Chi-square test - normality
ranksum - two sample
signrank - paired test
Kruskal-Wallis - N-sample
NO - Parametric and Non-parametric
Levene's test - variance
t-test - two-sample
paired t-test - paired test
ANOVA - N-sample
Kolmogorov-smirnov test - normality
Time/Mixed effect
rmANOVA - Nsample/repeated
Distribution difference
Chi-square
Exact Fisher Test
Prediction
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
Dimension Reduction Link
Multiway array decomposition
Perform Lit Search for how data is normally represented
Is the Raw Data Normally Distributed?
No
FIRST OPTION: Reorganize the Data
Find the Source of Variation (Scatterplots and Histograms)
If data is skewed
Is there reasonable way to group subjects to explain variation?
Find a relevant ratio for a linear transformation
Test again for normality
e.g. Weight
e.g. Sex
If Outliers are found
Can/Should outliers be removed?
Yes, there are enough subjects in the study for the power analysis
Remove and restart
No
Were outliers different in any way NOT related to the study?
1 more item...
Normalize data to each subject
1 more item...
SECOND OPTION: Transform the Data
Perform any common normalizations or transformation according to lit search
For proportions or Percentages
Arcsin transformation
When looking for a change from normalcy, not a specific increase or decrease
z-score transformation
For skewed data
Negative Skew
Exponential transformation (for log trends e.g. decay)
Power transformation (for power components e.g. area, volume)
Positive Skew with non-negative values
Logarithmic transformation (for cumulative, multiplicative effects or exponential data.
Square Root transformation (for counts or data with a power, e.g. area, volume)
Yes
Is this how the data should be represented?
Yes
Go to statistical analysis
Use parametric plot + scatter plots
No
Use non parametric plot
Explore data transformation
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
Ways to Look at and Interpret Data
Group Subjects by:
Sex
Do males and females perform differently either befofe and/or after treatment?
Treatment
How does recovery vary across time based on treatment? Were there any differences between groups before treatment?
Timepoint
In each group, do subjects show a change over time?