The Introduction to statistical thinking

1. Learning Journal Reflective  Comments:

December 26, 2021-Read part of chapter 7

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I started studying the weekly topic late. My learning began on Sunday when I read chapter seven’s textbook on sampling distribution. The sampling distribution is a collection of numbers that contains other samples Yakir, B. (2011). The topic was pervasive, so I did not complete it.

December 27, 2021- Continue reading Chapter 7

I completed reading the remaining part of the chapter on simulating sample distributions. I also practiced simulation using the for loop in the R studio with the examples provided in the textbook. At first, it looked challenging, but after trying it several times, I managed to understand it.

December 28, 2021- Repeated reading chapter 7 and Watched Video tutorials.

Chapter seven was quite complex, so I had to reread it to understand. Therefore, I went through the areas that I found difficult to understand. I also watched the video tutorials provided and completed the self-quiz.

December 29, 2021- Worked on the discussion assignment 

On Wednesday, I completed the discussion assignment, written assignment and submitted them. I also read, rated, and provided a substantial response to my peers’ posts.



2. Vocabulary and R functions

a) A sampling distribution is a collection of numbers that includes information about other samples. Yakir, B. (2011).

b) The law of large numbers is a mathematical result concerning the sample average’s sampling distribution. It states that when the sample size is large, the average distribution of measurements is highly concentrated in the vicinity of the measurement’s expectation Yakir, B. (2011).


Sampling distribution involves collecting figures representing the statistics of different samples. We can collect different samples containing various observations and calculate the statistics of each sample such as its mean, standard deviation, variance e.t.c and subject it into a sampling distribution. According to the law of large numbers, the more samples we measure their mean, the closer we get to the actual mean of the population.

The distribution of a sample involves the variability of the population under study.

According to Yakir, B. (2011, pg 122), the sampling distribution can be applied in the theoretical models such as binomial distribution. One example of the binomial distribution is buying a ticket to win a lottery. We can collect the results for 100 tickets and determine how many won and how many lost. We can approximate the sample average by simulating the 100 tickets by 100 times using the function “rbinom.” The bigger the sample, the closer the sample mean to the actual mean.


Yakir, B. (2011). Introduction to statistical thinking (with R, without calculus). The Hebrew University of Jerusalem. Retrieved from;




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