Data analysis: hypothesis testing
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Contents

  • Introduction
  • Learning outcomes
  • 1 Hypothesis testing
    • 1.1 Two types of hypothesis
    • 1.2 Business context
    • 1.3 Hypothesis formulation
  • 2 Alpha (α) levels
    • 2.1 Statistical significance
  • 3 One-tailed vs Two-tailed test
    • 3.1 Non-directional hypotheses
    • 3.2 Directional hypotheses
  • 4 Z-test vs t-test
    • 4.1 Z-test: known population standard deviation
    • 4.2 T-test: unknown population standard deviation
    • 4.3 Additional consideration
    • 4.4 Non-directional hypotheses
    • 4.5 Directional hypotheses
  • 5 Z Critical value and z-test
    • 5.1 One-tailed test
    • 5.2 Two-tailed test
    • 5.3 Practical example
    • 5.4 Finding Finding x-bar
  • 6 P-value
    • 6. 1 Calculating the p-value
  • 7 Hypothesis testing for population proportions
  • 7.1 Practical example
  • 8 T-test
    • 8.1 One sample t-tests: comparing a sample mean against the population mean
      • 8.1.1 Performing a One-Tailed One-Sample T-Test
      • 8.1.2 Performing a two-tailed one-sample T-test
      • 8.1.3 Practical Example
    • 8.2 P-value
  • 9 Summary
  • 10 Reflection
  • 11 Conclusion
  • References
  • Acknowledgements

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