# Introduction to Basic Statistical Methodology. CHAPTER 1 ~ Introduction ~

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Introduction to Basic Statistical Methodology

CHAPTER 1 ~ Introduction ~

Examples: Toasting time, Temperature settings, etc.… 3 What is “random variation” in the distribution of a population? POPULATION 1: Little to no variation (e.g., product manufacturing) In engineering situations such as this, we try to maintain “quality control”… i.e., “tight tolerance levels,” high precision, low variability. But what about a population of, say, people?

POPULATION 1: Little to no variation (e.g., product manufacturing) Density What is “random variation” in the distribution of a population? POPULATION 2: Little to no variation (e.g., clones) 4 Most individual values ≈ population mean value Examples: Gender, Race, Age, Height, Drug Response (e.g., cholesterol level),… NOT REALISTIC!!! Very little variation about the mean!

POPULATION 2: Little to no variation (e.g., clones)POPULATION 3: Much variation (more realistic) Examples: Gender, Race, Age, Height, Drug Response (e.g., cholesterol level),… What is “random variation” in the distribution of a population? Density 5 Much more variation about the mean!

GLOBAL OPERATION DYNAMICS, INC. What are “statistics,” and how can they be applied to real issues? Example: Suppose a certain company insists that it complies with “gender equality” regulations among its employee population, i.e., approx. 50% male and 50% female. 6   To test this claim, let us select a random sample of n = 100 employees, and count X = the number of males. (If the claim is true, then we expect X  50.)             etc. Sample size n partially depends on the power of the test, i.e., the desired probability of correctly rejecting a false null hypothesis (  80%). The larger the n, the higher the power.

GLOBAL OPERATION DYNAMICS, INC. What are “statistics,” and how can they be applied to real issues? Example: Suppose a certain company insists that it complies with “gender equality” regulations among its employee population, i.e., approx. 50% male and 50% female. 7   To test this claim, let us select a random sample of n = 100 employees, and count X = the number of males. (If the claim is true, then we expect X  50.)             etc. X = 64 males (+ 36 females) Questions: If the claim is true, how likely is this experimental result? (“p-value”) Could the difference (14 males) be due to random chance variation, or is it statistically significant?

GLOBAL OPERATION DYNAMICS, INC. What are “statistics,” and how can they be applied to real issues? Example: Suppose a certain company insists that it complies with “gender equality” regulations among its employee population, i.e., approx. 50% male and 50% female. 9   To test this claim, let us select a random sample of n = 100 employees, and count X = the number of males. (If the claim is true, then we expect X  50.)              etc. X = 64 males (+ 36 females) Questions: If the claim is true, how likely is this experimental result? (“p-value”) Could the difference (14 males) be due to random chance variation, or is it statistically significant? HYPOTHESIS EXPERIMENT OBSERVATIONS