Concept

Sample size determination

Related lectures (31)

Normal Distribution: Characteristics and Z-scores

Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.

Two-Sample T-Test

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

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Understanding Statistics & Experimental Design

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

Normal Distribution: Characteristics and Examples

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

Statistical Inference: Confidence Intervals

Covers the construction of approximate confidence intervals using the central limit theorem for large sample sizes.

Optimality and Asymptotics

Explores the optimality of the Least Squares Estimator and its large sample distribution.

Hypothesis Testing & Confidence Intervals

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

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

Central Limit Theorem: Illustration and Applications

Explores the Central Limit Theorem and its statistical implications in random variables.

Statistical Measures: Mean, Median, and Dispersion Techniques

Discusses statistical measures of central tendency and dispersion, focusing on mean, median, and their implications in data analysis.

Smith Theory in Floer Persistence and Dynamics

Covers Smith theory in Floer persistence and dynamics, exploring classical mechanics invariants, Poincaré-Birkhoff theorem, and the Hofer-Zehnder conjecture.

Estimating Relaxation Time: Variance and Chains

Covers the estimation of relaxation time in chains and the importance of sample sizes.

Stochastic Simulation: Monte Carlo Method

Covers the properties and error estimates of the Monte Carlo method in stochastic simulation.

Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Confidence Intervals and MLE Limit Theorems

Explores constructing confidence intervals and MLE limit theorems for large samples.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Sample Preparation for TEM Observation

Explores the methods and challenges of sample preparation for TEM observation, emphasizing the importance of size, composition, and safety measures.

Monte Carlo Estimation: Error Analysis

Covers the Monte Carlo method for generating realizations and unbiased estimators.