Data dredging

Formal sciences
Statistics
Statistical inference
Statistical hypothesis testing

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Related lectures (32)

Linear Models: Estimation and Inference

Covers the estimation and inference of linear models in statistical data science.

Hypothesis Testing: T-Test Methodology

Explores hypothesis testing methodology using t-tests and the randomization test.

Hypothesis Testing in Statistics

Explores hypothesis testing, significance levels, errors, GWAS, optimal testing, and point estimation in statistics.

Statistical Hypothesis Testing: Concepts and Applications

Provides an overview of statistical hypothesis testing, including its purpose, formulation, and significance in data analysis.

Local Spatial Autocorrelation

Explores local spatial autocorrelation for detecting clusters at a local level and assessing the spatial distribution of variables.

Gaussian Random Vectors

Explores Gaussian random vectors and their statistical properties, emphasizing the importance of specifying statistical properties in complex valued random vectors.

Hypothesis Testing: Q-Q Plots and Non-Parametric Tests

Covers hypothesis testing, Q-Q plots, and non-parametric tests in statistics.

Hypothesis Testing and Confidence Intervals: Key Concepts

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

Spatial Autocorrelation Analysis

Explores local spatial autocorrelation, Moran's I calculation, and spatial pattern significance in real-world data.

Nested Model Selection

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

Hypothesis Testing: T-Test and Chi-Square Test

Explains hypothesis testing using T-test and Chi-square test to compare means and assess independence of variables.

Maxwell's Equations: Practical Significance

Illustrates the practical implications of Maxwell's equations through real-life demonstrations.

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