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Statistical Mastery for Graduate Research
Program Title: From Data to Discovery: A Step-by-Step Guide to Applied Statistics
Target Audience: Master’s Degree Students (All Research Domains)
Duration: 4 Progressive Modules (approx. 24 contact hours)
Executive Summary
Graduate research is often hindered by a "calculation-first" approach that skips the critical logic of experimental design and probability. This program is designed as a progressive journey, ensuring students master foundational prerequisites before moving into advanced hypothesis testing. The goal is to move the researcher from passive data collection to credible, evidence-based discovery.
Training Objective
Training Objective
Establish Quantitative Literacy: Bridge the gap between raw data and meaningful summaries.
Integrate Design and Analysis: Teach students that the statistical test is chosen during the design phase, not after data collection.
Standardize Hypothesis Logic: Provide a clear framework for interpreting $p$-values, significance levels ($\alpha$), and confidence intervals.
Practical Tool Application: Enable students to select and execute the correct comparison tests (Independent vs. Paired) with precision.
Learning Outcome
Learning Outcome
Analyze Data Shape: Identify skewness and outliers to determine if data meets the assumptions of parametric tests.
Design Valid Experiments: Apply randomization and control variables to isolate specific effects.
Formulate Hypotheses: Construct clear Null ($H_0$) and Alternative ($H_1$) hypotheses for their specific research questions.
Execute Inferential Tests: Perform and interpret Independent and Paired t-tests.
Evaluate Risk: Distinguish between Type I (False Positive) and Type II (False Negative) errors.
Curriculum Break down
Training proposal
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