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Lecture
T-tests and Empirical Tests
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Related lectures (30)
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Two-Sample T-Test
Explains the two-sample t-test for comparing means of independent samples, including hypothesis testing steps and test statistic calculation.
Statistics: Hypothesis Testing & Confidence Intervals
Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.
Hypothesis Testing: State of Nature
Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.
Red Influence: Attractiveness, Desirability, and Status
Explores the effects of red on attractiveness, desirability, and status, emphasizing statistical analysis and the challenges of replication and publication bias.
Confidence Intervals and T-Test
Explores confidence intervals, T-test, and hypothesis testing, including assumptions and critical regions.
Hypothesis Testing: Statistics Overview
Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.
Statistical Analysis: Hypothesis Testing and Distribution Models
Covers statistical analysis techniques, hypothesis testing, and distribution models using R software.
Understanding Statistics & Experimental Design
Explores unequal variances, replication, power, effect size, biases, and their impact on research outcomes.
Experimental Research Design: T-Test Analysis
Explores digital lesson development, experimental research, t-tests, ANOVA, and statistical analysis using R.
Maximum Likelihood Estimation: Multivariate Statistics
Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.
