# 7.4 - Central Limit Theorem

7.4 - Central Limit TheoremAs we saw at the beginning of this lesson, many of the sampling distributions that you have constructed and worked with this semester are approximately normally distributed. The reason behind this is one of the most important theorems in statistics.

### Central Limit Theorem

The **Central Limit Theorem** states that if the sample size is sufficiently large then the sampling distribution will be approximately normally distributed for many frequently tested statistics, such as those that we have been working with in this course: one sample mean, one sample proportion, difference in two means, difference in two proportions, the slope of a simple linear regression model, and Pearson's *r* correlation.

Over the next few lessons we will examine what constitutes a "sufficiently large" sample size. Essentially, it is determined by the point at which the sampling distribution becomes approximately normal.

In practice, when we construct confidence intervals and conduct hypothesis tests we often use the normal distribution (or *t* distributions which you'll see next week) as opposed to bootstrapping or randomization procedures in situations when the sampling distribution is approximately normal. This method is preferred by many because z scores are on a standard scale (i.e., mean of 0 and standard deviation of 1) which makes interpreting results more straight forward.

Drag the slider at the bottom of the graph to see normal curve fit on the randomization plot.

# 7.4.1 - Hypothesis Testing

7.4.1 - Hypothesis Testing## Five Step Hypothesis Testing Procedure

In the remaining lessons, we will use the following five step hypothesis testing procedure. This is slightly different from the five step procedure that we used when conducting randomization tests.

**Check assumptions and write hypotheses.**The assumptions will vary depending on the test. In this lesson we'll be confirming that the sampling distribution is approximately normal by visually examining the randomization distribution. In later lessons you'll learn more objective assumptions. The null and alternative hypotheses will always be written in terms of population parameters; the null hypothesis will always contain the equality (i.e., \(=\)).**Calculate the test statistic.**Here, we'll be using the formula below for the general form of the test statistic.**Determine the p-value.**The p-value is the area under the standard normal distribution that is more extreme than the test statistic in the direction of the alternative hypothesis.**Make a decision.**If \(p \leq \alpha\) reject the null hypothesis. If \(p>\alpha\) fail to reject the null hypothesis.**State a "real world" conclusion.**Based on your decision in step 4, write a conclusion in terms of the original research question.

## General Form of a Test Statistic

When using a standard normal distribution (i.e., z distribution), the test statistic is the standardized value that is the boundary of the p-value. Recall the formula for a z score: \(z=\frac{x-\overline x}{s}\). The formula for a test statistic will be similar. When conducting a hypothesis test the sampling distribution will be centered on the null parameter and the standard deviation is known as the standard error.

- General Form of a Test Statistic
- \(test\;statistic=\dfrac{sample\;statistic-null\;parameter}{standard\;error}\)

This formula puts our observed sample statistic on a standard scale (e.g., z distribution). A z score tells us where a score lies on a normal distribution in standard deviation units. The test statistic tells us where our sample statistic falls on the sampling distribution in standard error units.

# 7.4.1.1 - Video Example: Mean Body Temperature

7.4.1.1 - Video Example: Mean Body Temperature**Research question: **Is the mean body temperature in the population different from 98.6° Fahrenheit?

# 7.4.1.2 - Video Example: Correlation Between Printer Price and PPM

7.4.1.2 - Video Example: Correlation Between Printer Price and PPM**Research question: **Is there a positive correlation in the population between the price of an ink jet printer and how many pages per minute (ppm) it prints?

# 7.4.1.3 - Example: Proportion NFL Coin Toss Wins

7.4.1.3 - Example: Proportion NFL Coin Toss Wins**Research question: **Is the proportion of NFL overtime coin tosses that are won different from 0.50?

StatKey was used to construct a randomization distribution:

#### Step 1: Check assumptions and write hypotheses

From the given StatKey output, the randomization distribution is approximately normal.

\(H_0\colon p=0.50\)

\(H_a\colon p \ne 0.50\)

#### Step 2: Calculate the test statistic

\(test\;statistic=\dfrac{sample\;statistic-null\;parameter}{standard\;error}\)

The sample statistic is the proportion in the original sample, 0.561. The null parameter is 0.50. And, the standard error is 0.024.

\(test\;statistic=\dfrac{0.561-0.50}{0.024}=\dfrac{0.061}{0.024}=2.542\)

#### Step 3: Determine the p value

The p value will be the area on the z distribution that is more extreme than the test statistic of 2.542, in the direction of the alternative hypothesis. This is a two-tailed test:

The p value is the area in the left and right tails combined: \(p=0.0055110+0.0055110=0.011022\)

#### Step 4: Make a decision

The p value (0.011022) is less than the standard 0.05 alpha level, therefore we reject the null hypothesis.

#### Step 5: State a "real world" conclusion

There is evidence that the proportion of all NFL overtime coin tosses that are won is different from 0.50

# 7.4.1.4 - Example: Proportion of Women Students

7.4.1.4 - Example: Proportion of Women Students**Research question**: Are more than 50% of all World Campus STAT 200 students women?

Data were collected from a representative sample of 501 World Campus STAT 200 students. In that sample, 284 students were women and 217 were not women.

StatKey was used to construct a sampling distribution using randomization methods:

Because this randomization distribution is approximately normal, we can find the p value by computing a standardized test statistic and using the z distribution.

#### Step 1: Check assumptions and write hypotheses

The assumption here is that the sampling distribution is approximately normal. From the given StatKey output, the randomization distribution is approximately normal.

\(H_0\colon p=0.50\)

\(H_a\colon p>0.50\)

#### 2. Calculate the test statistic

\(test\;statistic=\dfrac{sample\;statistic-hypothesized\;parameter}{standard\;error}\)

The sample statistic is \(\widehat p = 284/501 = 0.567\).

The hypothesized parameter is the value from the hypotheses: \(p_0=0.50\).

The standard error on the randomization distribution above is 0.022.

\(test\;statistic=\dfrac{0.567-0.50}{0.022}=3.045\)

#### 3. Determine the p value

We can find the p value by constructing a standard normal distribution and finding the area under the curve that is more extreme than our observed test statistic of 3.045, in the direction of the alternative hypothesis. In other words, \(P(z>3.045)\):

Our p value is 0.0011634

#### 4. Make a decision

Our p value is less than or equal to the standard 0.05 alpha level, therefore we reject the null hypothesis.

#### 5. State a "real world" conclusion

There is evidence that the proportion of all World Campus STAT 200 students who are women is greater than 0.50.

# 7.4.1.5 - Example: Mean Quiz Score

7.4.1.5 - Example: Mean Quiz Score**Research question: **Is the mean quiz score different from 14 in the population?

StatKey was used to construct a randomization distribution: