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If AI does it all, why should we formulate research hypotheses? — Foundations of quantitative research methods in the social sciences

Why should you formulate research hypotheses yourself when AI can even do the analysis for you? A 42-slide lecture that moves from the basic structure of a hypothesis to the conditions of scientific knowledge, deduction and induction, and the role of theory.

Graduate students who run analyses with AI but still struggle to explain why and how to formulate research hypotheses · Hallym University Media School, graduate program · Communication Research Methods II · November 3, 2026 · 2026.11.03

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A hypothesis-structure diagram slide showing H1 (research hypothesis/alternative hypothesis) and H0 (null hypothesis) side by side, with independent variable · dependent variable boxes

What this project covers

  • Write a hypothesis in the basic structure ‘if A increases, B also increases or decreases’ and as H1·H0.
  • Check the conditions of scientific knowledge through falsifiability · comprehensiveness · replicability.
  • Generalize your hypothesis through related studies and theory, and check the level of measurement of the variables.

Presentation outline

Expand the full outline
  1. Read the complete lecture
  2. 01. 9W-1
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  4. 02. How do you make a “hypothesis”?
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  6. 03. Summary of the whole process of setting and testing a “hypothesis”
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  8. 04. Example – “Women's average Netflix drama viewing time is longer than men's”
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  10. 05. Methodological foundations: methods of acquiring scientific knowledge
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  12. 06. Social-science communication: the convergence of the humanities and science
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  14. 07. The science of communication
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  16. 08. Benefits of communication research
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  18. 09. Benefits of communication research
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  20. 10. The problem with common sense – “Did you really double-check that claim?”
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  22. 11. The problem with common sense – “Did you really double-check that claim?”
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  24. 12. The problem with common sense and critical thinking
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  26. 13. Methodological foundations: conditions of scientific knowledge
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  28. 14. Condition 1 of scientific knowledge: “Falsifiability”
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  30. 15. Condition 1 of scientific knowledge: “Falsifiability”
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  32. 16. Condition 2 of scientific knowledge: “Comprehensiveness”
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  34. 17. Premise 4 of inferential statistics: we cannot pin down the population exactly, but we want to pin it down as accurately as possible
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  36. 18. Statistical estimation range: being “right” is not what matters
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  38. 19. Statistical estimation range: being “right” is not what matters
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  40. 20. Condition 3 of scientific knowledge: “Replicability”
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  42. 21. Methodological foundations: methods of acquiring scientific knowledge
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  44. 22. Methods of acquiring scientific knowledge
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  46. 23. Methods of acquiring scientific knowledge
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  48. 24. Methods of acquiring scientific knowledge
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  50. 25. Why is theory important?
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  52. 26. Let us grab some candy with our fists.
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  54. 27. Summary of the basics of inferential statistics
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  56. 28. Examples of hypotheses
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  58. 29. Examples of hypotheses
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  60. 30. Examples of hypotheses
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  62. 31. RQ. How many strawberry chocolate candies are there in total among all the original candies?
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  64. 32. Why is theory important?
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  66. 33. Why is theory important?
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  68. 34. Why is theory important?
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  70. 35. Why is theory important?
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  72. 36. Why is theory important?
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  74. 37. Why is theory important?
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  76. 38. Why is theory important?
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  78. 39. Summary of the whole process of setting and testing a “hypothesis”
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  80. 40. “What effect do activities that raise university students' self-efficacy have on achievement?”
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  82. 41. Examples of hypotheses you can use in a research proposal
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  84. 42. This week's assignment
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