Statistics & Experimental Design

Related lectures (29)

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Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Explains the two-sample t-test for comparing means of independent samples, including hypothesis testing steps and test statistic calculation.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Covers hypothesis testing, power, confidence intervals, and small sample considerations.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Covers basic probability theory, signal detection theory, statistics, and meta-statistics, explaining effect sizes, power, and hypothesis testing.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.