Author: Vincent Granville

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC.

**29 Statistical Concepts Explained in Simple English**

- Parametric Statistics, Tests and Data
- Pareto Distribution Definition
- Parsimonious Model: Definition, Ways to Compare Models
- Partial Correlation & Semi-Partial: Definition & Example
- Pearson Mode Skewness: Definition and Formulas
- Pearson’s Coefficient of Skewness
- Percent Error & Percent Difference: Definition & Examples
- Z score to Percentile Calculator and Manual Methods
- Performance Bias: Definition and Examples
- Permuted Block Randomization
- PERT Distribution / Beta-PERT: Definition, Examples
- Phi Coefficient (Mean Square Contingency Coefficient)
- Pillai’s Trace
- Point-Biserial Correlation & Biserial Correlation: Definition, Examples
- Point Estimate: Definition
- Poisson Distribution / Poisson Curve: Simple Definition
- Pooled Standard Deviation
- Population Density Definition
- Population Mean Definition
- Population Proportion
- Population Variance: Definition and Examples
- Posterior Probability & the Posterior Distribution
- Post-Hoc Definition and Types of Post Hoc Tests
- Power Law and Power Law Distribution
- Practice Effect & Carry Over Effect Definition & Examples
- Prediction Interval: Simple Definition, Examples
- Predictive Validity
- Primary Data & Secondary Data: Definition & Example
- Probabilistic: Definition, Models and Theory Explained

Previous editions, in alphabetical order, can be accessed here:

Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 6* |* Part 7* |* Part 8 | Part 9 | Part 10 | Part 11 | Part 12

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