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Can the candies in my fist tell me about the whole box? — Foundations of descriptive and inferential statistics

What does it take to speak about the whole box (the population) from a few candies drawn with your fist (a sample)? A 77-slide lecture that has you compute the mode · median · mean and variance · standard deviation directly in R, and that carries on to the trap of the mean and the starting point of inferential statistics, which estimates a population from a sample.

Graduate students who compute means in R but still struggle to explain what changes when a sample's numbers are used to speak about a population · Hallym University Media School · Graduate Communication Research Methods II · November 10, 2026 · 2026.11.10

Open presentation ↗ Read on the web
The ‘Population and sample – candy-drawing simulator’ slide: under the question “What I drew with my fist came out as nothing but strawberry chocolates – but the box had an even mix?”, a candy-drawing simulator screen, a fist and a few candies, and a photo of the candy box (population)

What this project covers

  • Distinguish population · sample · parameter · statistic, and explain what descriptive and inferential statistics each do.
  • Compute the mode · median · mean and variance · standard deviation in R, and examine the trap of the mean, in which extreme values pull the mean toward them.
  • Understand why the standard error and confidence intervals are needed when estimating a population from sample statistics.

Presentation outline

Expand the full outline
  1. Read the complete lecture
  2. 01. 10W-1
  3. Supporting text from the original screen
  4. 02. Population and sample – a Halloween random candy and chocolate box
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  6. 03. Let us grab some candy with our fists.
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  8. 04. Let us grab some candy with our fists.
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  10. 05. Population and sample
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  12. 06. Descriptive statistics and inferential statistics
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  14. 07. What is descriptive statistics?
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  16. 08. Descriptive statistics
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  18. 09. Descriptive statistics
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  20. 10. Population and sample – candy-drawing simulator
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  22. 11. Descriptive statistics
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  24. 12. Types and interpretation of descriptive statistics – what to use for a ‘one-line summary’
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  26. 13. Types and interpretation of descriptive statistics
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  28. 14. Types and interpretation of descriptive statistics
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  30. 15. Why R?
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  32. 16. Installing R & R-Studio
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  34. 17. Installing R & R-Studio
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  36. 18. Installing R & R-Studio
  37. Supporting text from the original screen
  38. 19. Installing R & R-Studio
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  40. 20. Installing R & R-Studio
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  42. 21. Installing R & R-Studio
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  44. 22. Running R-Studio
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  46. 23. Running R-Studio
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  48. 24. Running R-Studio
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  50. 25. Running R-Studio: Ctrl+C / Ctrl+V the following code as is
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  52. 26. Running R-Studio: Ctrl+C / Ctrl+V the following code as is
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  54. 27. Example data for descriptive statistics
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  56. 28. Types and interpretation of descriptive statistics - mode
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  58. 29. AI & R Exercise - Mode
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  60. 30. Example data for descriptive statistics – the answer for the mode
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  62. 31. Types and interpretation of descriptive statistics - median
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  64. 32. AI & R Exercise - Median
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  66. 33. Example data for descriptive statistics – the answer for the median
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  68. 34. Types and interpretation of descriptive statistics - mean
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  70. 35. AI & R Exercise - Mean
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  72. 36. Example data for descriptive statistics – the answer for the mean value
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  74. 37. The trap of the mean
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  76. 38. Example data for descriptive statistics – the problem with the mean value: Outlier
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  78. 39. R-Studio example 2: Ctrl+C / Ctrl+V the following code as is
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  80. 40. R-Studio example 2: salary
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  82. 41. AI & R Exercise - Mean
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  84. 42. R-Studio example 2: the answer for the average salary
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  86. 43. The trap of the mean
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  88. 44. Median income in Korea
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  90. 45. Trends in Korea's standard median income (by household size)
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  92. 46. Types and interpretation of descriptive statistics
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  94. 47. Types and interpretation of descriptive statistics
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  96. 48. Types and interpretation of descriptive statistics
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  98. 49. Types and interpretation of descriptive statistics
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  100. 50. R-Studio example 2: Ctrl+C / Ctrl+V the following code as is
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  102. 51. R-Studio example 2: distribution of the average salary with a student earning 1 billion won mixed in
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  104. 52. R-Studio example 2: Ctrl+C / Ctrl+V the following code as is
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  106. 53. R-Studio example 2: normal distribution plot of students with evenly spread salaries
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  108. 54. Summary
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  110. 55. What is inferential statistics?
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  112. 56. What is inferential statistics?
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  114. 57. Population and sample – how many strawberry chocolates are there in the whole box?
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  116. 58. Let us grab some candy with our fists.
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  118. 59. RQ. How many strawberry chocolate candies are there in total among all the original candies?
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  120. 60. RQ. How many strawberry chocolate candies are there in total among all the original candies?
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  122. 61. Let us count only the candies in my hand (never mind the original box)
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  124. 62. Let us count only the candies in my hand (never mind the original box)
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  126. 63. Candy-drawing simulator – try drawing on the web
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  128. 64. Exercise. Let us count the number of candies and the strawberry chocolates with the candy-drawing simulator
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  130. 65. RQ. How many strawberry chocolate candies are there in total among all the original candies?
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  132. 66. RQ. How many strawberry chocolate candies are there in total among all the original candies?
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  134. 67. RQ. How many strawberry chocolate candies are there in total among all the original candies?
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  136. 68. Finding the descriptive statistics of the strawberry chocolates
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  138. 69. Finding the descriptive statistics of the strawberry chocolates – R output
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  140. 70. Inferential statistics: estimating the number of strawberry chocolates in the whole box
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  142. 71. Inferential statistics: estimating the number of strawberry chocolates in the whole box
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  144. 72. The beginning of inferential statistics: sampling and estimation
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  146. 73. Inferential statistics: estimating the population (the candy box) from candy sample statistics
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  148. 74. Premise 1 of inferential statistics: the population cannot be known ‘exactly’
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  150. 75. Premise 2 of inferential statistics: there is always a chance of being wrong
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  152. 76. Premise 3 of inferential statistics: a ‘standard error’ exists (a number that shows how wrong I am)
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  154. 77. Summary of the basics of inferential statistics
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