Exam Two Review Summary

Exam Two Review Summary

  • General Overview

    • Review of key topics for upcoming exam.

  • Survey Insights

    • Collected data via Qualtrics on topics needing more review:

    • Focus on t-tests (single sample, independent, and dependent).

    • Request for more conceptual overview of sampling distributions.

  • Topics of Focus

    • Calculating confidence intervals.

    • Overview of effect sizes (not tested this exam).

    • Formula memorization: major equations will be provided.

  • Statistical Concepts

    • Sampling Distribution:

    • Theoretical dataset representing distributions of all possible sample means.

    • Standard Error: Estimate of the average sampling error when estimating population parameters.

    • Calculated using population standard deviation (if known): SE=σnSE = \frac{\sigma}{\sqrt{n}}; or using sample standard deviation: SE=snSE = \frac{s}{\sqrt{n}}.

    • Central Limit Theorem: As sample size increases, sampling distribution approaches normal distribution.

  • t-tests Overview

    • Types of t-tests:

    • Z-test: Known population mean and standard deviation.

    • One-Sample t-test: Known population mean, unknown standard deviation.

    • Independent Samples t-test: Two separate groups with unknown population parameters.

    • Dependent Samples t-test: Same group tested under different conditions.

    • Understand appropriate test selection based on sample and population parameters.

  • Confidence Intervals

    • Range within which the true population mean is likely to fall.

    • Often calculated using critical values and standard error.

    • Basic formula: CI=xˉ±(Critical Value×SE)CI = \bar{x} \pm (Critical\ Value \times SE).

  • Important Findings

    • Critical values from t-distribution depend on degrees of freedom, use tables as reference.

    • Differences between sample means tested for significance.

    • Conceptual understanding of critical regions (alpha levels for one-tailed vs two-tailed tests).