In August of 2012, news outlets ranging from the Washington Post to the Huffington Post ran a story about the rise of atheism in America. The source for the story was a poll that asked people, “Irrespective of whether you attend a place of worship or not, would you say you are a religious person, not a religious person or a convinced atheist?” This type of question, which asks people to classify themselves in one way or another, is common in polling and generates categorical data. In this lab we take a look at the atheism survey and explore what’s at play when making inference about population proportions using categorical data.

The survey

To access the press release for the poll, conducted by WIN-Gallup International, click on the following link:

https://github.com/jbryer/DATA606/blob/master/inst/labs/Lab6/more/Global_INDEX_of_Religiosity_and_Atheism_PR__6.pdf

Take a moment to review the report then address the following questions.

  1. In the first paragraph, several key findings are reported. Do these percentages appear to be sample statistics (derived from the data sample) or population parameters?

Answer: These data are from a poll that conducted among sample group. There for these percentages appear to be sample statistics.

  1. The title of the report is “Global Index of Religiosity and Atheism”. To generalize the report’s findings to the global human population, what must we assume about the sampling method? Does that seem like a reasonable assumption?

Answer: We must assume that these sample populations are randomly picked and as stated more than 50,000 men and women from 57 counries making that sample less than 10% of global population. This is seems like a reasonable assumption as we are comparing a global population not a one country or an area.

The data

Turn your attention to Table 6 (pages 15 and 16), which reports the sample size and response percentages for all 57 countries. While this is a useful format to summarize the data, we will base our analysis on the original data set of individual responses to the survey. Load this data set into R with the following command.

download.file("http://www.openintro.org/stat/data/atheism.RData",
              destfile = "atheism.RData")
load("atheism.RData")
head(atheism, 100)
##     nationality    response year
## 1   Afghanistan non-atheist 2012
## 2   Afghanistan non-atheist 2012
## 3   Afghanistan non-atheist 2012
## 4   Afghanistan non-atheist 2012
## 5   Afghanistan non-atheist 2012
## 6   Afghanistan non-atheist 2012
## 7   Afghanistan non-atheist 2012
## 8   Afghanistan non-atheist 2012
## 9   Afghanistan non-atheist 2012
## 10  Afghanistan non-atheist 2012
## 11  Afghanistan non-atheist 2012
## 12  Afghanistan non-atheist 2012
## 13  Afghanistan non-atheist 2012
## 14  Afghanistan non-atheist 2012
## 15  Afghanistan non-atheist 2012
## 16  Afghanistan non-atheist 2012
## 17  Afghanistan non-atheist 2012
## 18  Afghanistan non-atheist 2012
## 19  Afghanistan non-atheist 2012
## 20  Afghanistan non-atheist 2012
## 21  Afghanistan non-atheist 2012
## 22  Afghanistan non-atheist 2012
## 23  Afghanistan non-atheist 2012
## 24  Afghanistan non-atheist 2012
## 25  Afghanistan non-atheist 2012
## 26  Afghanistan non-atheist 2012
## 27  Afghanistan non-atheist 2012
## 28  Afghanistan non-atheist 2012
## 29  Afghanistan non-atheist 2012
## 30  Afghanistan non-atheist 2012
## 31  Afghanistan non-atheist 2012
## 32  Afghanistan non-atheist 2012
## 33  Afghanistan non-atheist 2012
## 34  Afghanistan non-atheist 2012
## 35  Afghanistan non-atheist 2012
## 36  Afghanistan non-atheist 2012
## 37  Afghanistan non-atheist 2012
## 38  Afghanistan non-atheist 2012
## 39  Afghanistan non-atheist 2012
## 40  Afghanistan non-atheist 2012
## 41  Afghanistan non-atheist 2012
## 42  Afghanistan non-atheist 2012
## 43  Afghanistan non-atheist 2012
## 44  Afghanistan non-atheist 2012
## 45  Afghanistan non-atheist 2012
## 46  Afghanistan non-atheist 2012
## 47  Afghanistan non-atheist 2012
## 48  Afghanistan non-atheist 2012
## 49  Afghanistan non-atheist 2012
## 50  Afghanistan non-atheist 2012
## 51  Afghanistan non-atheist 2012
## 52  Afghanistan non-atheist 2012
## 53  Afghanistan non-atheist 2012
## 54  Afghanistan non-atheist 2012
## 55  Afghanistan non-atheist 2012
## 56  Afghanistan non-atheist 2012
## 57  Afghanistan non-atheist 2012
## 58  Afghanistan non-atheist 2012
## 59  Afghanistan non-atheist 2012
## 60  Afghanistan non-atheist 2012
## 61  Afghanistan non-atheist 2012
## 62  Afghanistan non-atheist 2012
## 63  Afghanistan non-atheist 2012
## 64  Afghanistan non-atheist 2012
## 65  Afghanistan non-atheist 2012
## 66  Afghanistan non-atheist 2012
## 67  Afghanistan non-atheist 2012
## 68  Afghanistan non-atheist 2012
## 69  Afghanistan non-atheist 2012
## 70  Afghanistan non-atheist 2012
## 71  Afghanistan non-atheist 2012
## 72  Afghanistan non-atheist 2012
## 73  Afghanistan non-atheist 2012
## 74  Afghanistan non-atheist 2012
## 75  Afghanistan non-atheist 2012
## 76  Afghanistan non-atheist 2012
## 77  Afghanistan non-atheist 2012
## 78  Afghanistan non-atheist 2012
## 79  Afghanistan non-atheist 2012
## 80  Afghanistan non-atheist 2012
## 81  Afghanistan non-atheist 2012
## 82  Afghanistan non-atheist 2012
## 83  Afghanistan non-atheist 2012
## 84  Afghanistan non-atheist 2012
## 85  Afghanistan non-atheist 2012
## 86  Afghanistan non-atheist 2012
## 87  Afghanistan non-atheist 2012
## 88  Afghanistan non-atheist 2012
## 89  Afghanistan non-atheist 2012
## 90  Afghanistan non-atheist 2012
## 91  Afghanistan non-atheist 2012
## 92  Afghanistan non-atheist 2012
## 93  Afghanistan non-atheist 2012
## 94  Afghanistan non-atheist 2012
## 95  Afghanistan non-atheist 2012
## 96  Afghanistan non-atheist 2012
## 97  Afghanistan non-atheist 2012
## 98  Afghanistan non-atheist 2012
## 99  Afghanistan non-atheist 2012
## 100 Afghanistan non-atheist 2012
summary(atheism)
##              nationality           response          year     
##  Pakistan          : 5409   atheist    : 5498   Min.   :2005  
##  France            : 3359   non-atheist:82534   1st Qu.:2005  
##  Korea, Rep (South): 3047                       Median :2012  
##  Ghana             : 2995                       Mean   :2009  
##  Macedonia         : 2418                       3rd Qu.:2012  
##  Peru              : 2414                       Max.   :2012  
##  (Other)           :68390
  1. What does each row of Table 6 correspond to? What does each row of atheism correspond to?

Answer: Each raw of Table 6 correspond to summary of each country data with percentage results of the four categories: A religious person, Not a religious person, A convinced atheist, Don’t know/no response. But in each row of ‘atheism’ corresponds to each individual person in the sample dataset of whole 57 countries and their atheist and non-atheist response.

To investigate the link between these two ways of organizing this data, take a look at the estimated proportion of atheists in the United States. Towards the bottom of Table 6, we see that this is 5%. We should be able to come to the same number using the atheism data.

  1. Using the command below, create a new dataframe called us12 that contains only the rows in atheism associated with respondents to the 2012 survey from the United States. Next, calculate the proportion of atheist responses. Does it agree with the percentage in Table 6? If not, why?
us12 <- subset(atheism, nationality == "United States" & year == "2012")

First I check the summary of us12 data subset to figure out its variables.

summary(us12)
##         nationality          response        year     
##  United States:1002   atheist    : 50   Min.   :2012  
##  Afghanistan  :   0   non-atheist:952   1st Qu.:2012  
##  Argentina    :   0                     Median :2012  
##  Armenia      :   0                     Mean   :2012  
##  Australia    :   0                     3rd Qu.:2012  
##  Austria      :   0                     Max.   :2012  
##  (Other)      :   0

Then I calculated the percentage for the proportion of atheists responses.

Proportion_atheist_us <- (sum(us12$response == "atheist")/sum(us12$nationality == "United States"))*100

round(Proportion_atheist_us,0)
## [1] 5

Answer: Data in Table 6 is same as the calculated proportion percentage of atheist in US which is 5%.

Inference on proportions

As was hinted at in Exercise 1, Table 6 provides statistics, that is, calculations made from the sample of 51,927 people. What we’d like, though, is insight into the population parameters. You answer the question, “What proportion of people in your sample reported being atheists?” with a statistic; while the question “What proportion of people on earth would report being atheists” is answered with an estimate of the parameter.

The inferential tools for estimating population proportion are analogous to those used for means in the last chapter: the confidence interval and the hypothesis test.

  1. Write out the conditions for inference to construct a 95% confidence interval for the proportion of atheists in the United States in 2012. Are you confident all conditions are met?

Answer:
>Random: As stated in the survey the samples are randomly picked so it does satisfy the random condition.
>Normal: In this case, sample size is sufficiently large fulfilling the success-failure condition. Both atheist(success) and non-ashiest(failure) numbers are more than 10.(np>=10 and n(1-p)>=10)
>Independent: This condition is satisfied as we are assuming that the sampling without replacement and the sample (1002) is less than 10% of the population.

If the conditions for inference are reasonable, we can either calculate the standard error and construct the interval by hand, or allow the inference function to do it for us.

inference(us12$response, est = "proportion", type = "ci", method = "theoretical", 
          success = "atheist")
## Single proportion -- success: atheist 
## Summary statistics:

## p_hat = 0.0499 ;  n = 1002 
## Check conditions: number of successes = 50 ; number of failures = 952 
## Standard error = 0.0069 
## 95 % Confidence interval = ( 0.0364 , 0.0634 )

Note that since the goal is to construct an interval estimate for a proportion, it’s necessary to specify what constitutes a “success”, which here is a response of "atheist".

Although formal confidence intervals and hypothesis tests don’t show up in the report, suggestions of inference appear at the bottom of page 7: “In general, the error margin for surveys of this kind is \(\pm\) 3-5% at 95% confidence”.

  1. Based on the R output, what is the margin of error for the estimate of the proportion of the proportion of atheists in US in 2012?
z <- 1.96
se <- 0.0069
me <- z*se
me
## [1] 0.013524
  1. Using the inference function, calculate confidence intervals for the proportion of atheists in 2012 in two other countries of your choice, and report the associated margins of error. Be sure to note whether the conditions for inference are met. It may be helpful to create new data sets for each of the two countries first, and then use these data sets in the inference function to construct the confidence intervals. Answer:
    As my first country of choice I will take China. It has the highest percentage of atheists. Fist I will create a subset for the data.
china12 <- subset(atheism, nationality == "China" & year == "2012")

Conditions for inference:
>Independent and Random conditions are me as the 500 of samples are randomly picked and it is less than 10% of the population. Also success-failure condition is me as there are 235 successes and 265 failures making it more than 10. There for conditions for inference are met.

Using inference function, calculating its standard error and 95% confidence interval.

inference(china12$response, est = "proportion", type = "ci", method = "theoretical", 
          success = "atheist")
## Single proportion -- success: atheist 
## Summary statistics:

## p_hat = 0.47 ;  n = 500 
## Check conditions: number of successes = 235 ; number of failures = 265 
## Standard error = 0.0223 
## 95 % Confidence interval = ( 0.4263 , 0.5137 )

Finding margin of error

se_china <- 0.0223
me_china <- z*se_china
me_china
## [1] 0.043708

As my second choice I pick India. Lets create a subset and calculate SE and confidence interval using inference function.

india12 <- subset(atheism, nationality == "India" & year == "2012")

Using inference function

inference(india12$response, est = "proportion", type = "ci", method = "theoretical", 
          success = "atheist")
## Single proportion -- success: atheist 
## Summary statistics:

## p_hat = 0.0302 ;  n = 1092 
## Check conditions: number of successes = 33 ; number of failures = 1059 
## Standard error = 0.0052 
## 95 % Confidence interval = ( 0.0201 , 0.0404 )

randomly chosen sample of 1091 satisfy its conditions for independent and random.Also data are normal as it satisfy the success-failure condition where it is more than 10. (successes = 33, failures = 1059)

Next step is to calculate its margin of error

india_se <- 0.0052
india_me <- z * se
india_me
## [1] 0.013524

How does the proportion affect the margin of error?

Imagine you’ve set out to survey 1000 people on two questions: are you female? and are you left-handed? Since both of these sample proportions were calculated from the same sample size, they should have the same margin of error, right? Wrong! While the margin of error does change with sample size, it is also affected by the proportion.

Think back to the formula for the standard error: \(SE = \sqrt{p(1-p)/n}\). This is then used in the formula for the margin of error for a 95% confidence interval: \(ME = 1.96\times SE = 1.96\times\sqrt{p(1-p)/n}\). Since the population proportion \(p\) is in this \(ME\) formula, it should make sense that the margin of error is in some way dependent on the population proportion. We can visualize this relationship by creating a plot of \(ME\) vs. \(p\).

The first step is to make a vector p that is a sequence from 0 to 1 with each number separated by 0.01. We can then create a vector of the margin of error (me) associated with each of these values of p using the familiar approximate formula (\(ME = 2 \times SE\)). Lastly, we plot the two vectors against each other to reveal their relationship.

n <- 1000
p <- seq(0, 1, 0.01)
me <- 2 * sqrt(p * (1 - p)/n)
plot(me ~ p, ylab = "Margin of Error", xlab = "Population Proportion")

  1. Describe the relationship between p and me.

Answer: me increases when p increase and after certain point me decreases with the p. It has a quadratic relationship.

Success-failure condition

The textbook emphasizes that you must always check conditions before making inference. For inference on proportions, the sample proportion can be assumed to be nearly normal if it is based upon a random sample of independent observations and if both \(np \geq 10\) and \(n(1 - p) \geq 10\). This rule of thumb is easy enough to follow, but it makes one wonder: what’s so special about the number 10?

The short answer is: nothing. You could argue that we would be fine with 9 or that we really should be using 11. What is the “best” value for such a rule of thumb is, at least to some degree, arbitrary. However, when \(np\) and \(n(1-p)\) reaches 10 the sampling distribution is sufficiently normal to use confidence intervals and hypothesis tests that are based on that approximation.

We can investigate the interplay between \(n\) and \(p\) and the shape of the sampling distribution by using simulations. To start off, we simulate the process of drawing 5000 samples of size 1040 from a population with a true atheist proportion of 0.1. For each of the 5000 samples we compute \(\hat{p}\) and then plot a histogram to visualize their distribution.

p <- 0.1
n <- 1040
p_hats <- rep(0, 5000)

for(i in 1:5000){
  samp <- sample(c("atheist", "non_atheist"), n, replace = TRUE, prob = c(p, 1-p))
  p_hats[i] <- sum(samp == "atheist")/n
}

hist(p_hats, main = "p = 0.1, n = 1040", xlim = c(0, 0.18))

These commands build up the sampling distribution of \(\hat{p}\) using the familiar for loop. You can read the sampling procedure for the first line of code inside the for loop as, “take a sample of size \(n\) with replacement from the choices of atheist and non-atheist with probabilities \(p\) and \(1 - p\), respectively.” The second line in the loop says, “calculate the proportion of atheists in this sample and record this value.” The loop allows us to repeat this process 5,000 times to build a good representation of the sampling distribution.

  1. Describe the sampling distribution of sample proportions at \(n = 1040\) and \(p = 0.1\). Be sure to note the center, spread, and shape.
    Hint: Remember that R has functions such as mean to calculate summary statistics.
mean(p_hats);
## [1] 0.09969
sd(p_hats)
## [1] 0.009287382

The sampling distribution centers at 0.1 with a symmetric normal distribution.

library(DATA606)
## Loading required package: shiny
## Loading required package: OIdata
## Loading required package: RCurl
## Loading required package: bitops
## Loading required package: maps
## Loading required package: ggplot2
## 
## Attaching package: 'ggplot2'
## The following object is masked from 'package:openintro':
## 
##     diamonds
## Loading required package: markdown
## 
## Welcome to CUNY DATA606 Statistics and Probability for Data Analytics 
## This package is designed to support this course. The text book used 
## is OpenIntro Statistics, 3rd Edition. You can read this by typing 
## vignette('os3') or visit www.OpenIntro.org. 
##  
## The getLabs() function will return a list of the labs available. 
##  
## The demo(package='DATA606') will list the demos that are available.
## 
## Attaching package: 'DATA606'
## The following object is masked _by_ '.GlobalEnv':
## 
##     inference
## The following object is masked from 'package:utils':
## 
##     demo
qqnormsim(p_hats)

  1. Repeat the above simulation three more times but with modified sample sizes and proportions: for \(n = 400\) and \(p = 0.1\), \(n = 1040\) and \(p = 0.02\), and \(n = 400\) and \(p = 0.02\). Plot all four histograms together by running the par(mfrow = c(2, 2)) command before creating the histograms. You may need to expand the plot window to accommodate the larger two-by-two plot. Describe the three new sampling distributions. Based on these limited plots, how does \(n\) appear to affect the distribution of \(\hat{p}\)? How does \(p\) affect the sampling distribution?
p <- 0.1
n <- 400
p_hats_2 <- rep(0, 5000)

for (i in 1:5000) {
    samp <- sample(c("atheist", "non_atheist"), n, replace = TRUE, 
        prob = c(p, 1 - p))
    p_hats_2[i] <- sum(samp == "atheist")/n
}

p <- 0.02
n <- 1040
p_hats_3 <- rep(0, 5000)

for (i in 1:5000) {
    samp <- sample(c("atheist", "non_atheist"), n, replace = TRUE, 
        prob = c(p, 1 - p))
    p_hats_3[i] <- sum(samp == "atheist")/n
}

p <- 0.02
n <- 400
p_hats_4 <- rep(0, 5000)

for (i in 1:5000) {
    samp <- sample(c("atheist", "non_atheist"), n, replace = TRUE, 
        prob = c(p, 1 - p))
    p_hats_4[i] <- sum(samp == "atheist")/n
}

par(mfrow = c(2, 2))
hist(p_hats, main = "p = 0.1, n = 1040", xlim = c(0, 0.18))
hist(p_hats_2, main = "p = 0.1, n = 400", xlim = c(0, 0.18))
hist(p_hats_3, main = "p = 0.02, n = 1040", xlim = c(0, 0.18))
hist(p_hats_4, main = "p = 0.02, n = 400", xlim = c(0, 0.18))

Once you’re done, you can reset the layout of the plotting window by using the command par(mfrow = c(1, 1)) command or clicking on “Clear All” above the plotting window (if using RStudio). Note that the latter will get rid of all your previous plots.

par(mfrow = c(1, 1))
  1. If you refer to Table 6, you’ll find that Australia has a sample proportion of 0.1 on a sample size of 1040, and that Ecuador has a sample proportion of 0.02 on 400 subjects. Let’s suppose for this exercise that these point estimates are actually the truth. Then given the shape of their respective sampling distributions, do you think it is sensible to proceed with inference and report margin of errors, as the reports does?
aus12 <- subset(atheism, nationality == "Australia" & year == 
    "2012")

summary(aus12)
##       nationality          response        year     
##  Australia  :1039   atheist    :104   Min.   :2012  
##  Afghanistan:   0   non-atheist:935   1st Qu.:2012  
##  Argentina  :   0                     Median :2012  
##  Armenia    :   0                     Mean   :2012  
##  Austria    :   0                     3rd Qu.:2012  
##  Azerbaijan :   0                     Max.   :2012  
##  (Other)    :   0

Answer: Number of atheists(successes) are 104 and number of non-atheists (failures) 935 for the Australian responses. This meets the minimum requirement to proceed.

ecuador12 <- subset(atheism, nationality == "Ecuador" & year == "2012")

summary(ecuador12)
##       nationality         response        year     
##  Ecuador    :404   atheist    :  8   Min.   :2012  
##  Afghanistan:  0   non-atheist:396   1st Qu.:2012  
##  Argentina  :  0                     Median :2012  
##  Armenia    :  0                     Mean   :2012  
##  Australia  :  0                     3rd Qu.:2012  
##  Austria    :  0                     Max.   :2012  
##  (Other)    :  0

Answer: Number of athiests (successes) 8 and number of non-atheists (failures) 396 for Ecuador responses. success-failure condition is not met as the number of successes are not at least 10.

On your own

The question of atheism was asked by WIN-Gallup International in a similar survey that was conducted in 2005. (We assume here that sample sizes have remained the same.) Table 4 on page 13 of the report summarizes survey results from 2005 and 2012 for 39 countries.

spain05 <- subset(atheism, nationality == "Spain" & year == "2005")
spain12 <- subset(atheism, nationality == "Spain" & year == "2012")
summary(spain05); summary(spain12)
##       nationality          response         year     
##  Spain      :1146   atheist    : 115   Min.   :2005  
##  Afghanistan:   0   non-atheist:1031   1st Qu.:2005  
##  Argentina  :   0                      Median :2005  
##  Armenia    :   0                      Mean   :2005  
##  Australia  :   0                      3rd Qu.:2005  
##  Austria    :   0                      Max.   :2005  
##  (Other)    :   0
##       nationality          response         year     
##  Spain      :1145   atheist    : 103   Min.   :2012  
##  Afghanistan:   0   non-atheist:1042   1st Qu.:2012  
##  Argentina  :   0                      Median :2012  
##  Armenia    :   0                      Mean   :2012  
##  Australia  :   0                      3rd Qu.:2012  
##  Austria    :   0                      Max.   :2012  
##  (Other)    :   0
inference(spain05$response, est = "proportion", type = "ci", method = "theoretical", 
    success = "atheist")
## Single proportion -- success: atheist 
## Summary statistics:

## p_hat = 0.1003 ;  n = 1146 
## Check conditions: number of successes = 115 ; number of failures = 1031 
## Standard error = 0.0089 
## 95 % Confidence interval = ( 0.083 , 0.1177 )
inference(spain12$response, est = "proportion", type = "ci", method = "theoretical", 
    success = "atheist")
## Single proportion -- success: atheist 
## Summary statistics:

## p_hat = 0.09 ;  n = 1145 
## Check conditions: number of successes = 103 ; number of failures = 1042 
## Standard error = 0.0085 
## 95 % Confidence interval = ( 0.0734 , 0.1065 )

Answer: In 2005 number of successes(atheist) are 115 and number of failures(non-atheists) are 1031 in Spain responses. In 2012 the number of successes(atheist) are 103 and number of failures(non-atheists) are 1042 for the same country. In both cases, success-failure conditions are met. 95% confidence interval for the true proportion of atheists are (0.083 , 0.1177) for 2005 and (0.0734 , 0.1065) for 2012. Because there is an overlap this does not provide convincing evidence that Spain has seen a change in itsm atheism index between 2005 and 2012.

**b.** Is there convincing evidence that the United States has seen a
change in its atheism index between 2005 and 2012? 
usa05 <- subset(atheism, nationality == "United States" & year == "2005")
usa12 <- subset(atheism, nationality == "United States" & year == "2012")
summary(usa05)
##         nationality          response        year     
##  United States:1002   atheist    : 10   Min.   :2005  
##  Afghanistan  :   0   non-atheist:992   1st Qu.:2005  
##  Argentina    :   0                     Median :2005  
##  Armenia      :   0                     Mean   :2005  
##  Australia    :   0                     3rd Qu.:2005  
##  Austria      :   0                     Max.   :2005  
##  (Other)      :   0
summary(usa12)
##         nationality          response        year     
##  United States:1002   atheist    : 50   Min.   :2012  
##  Afghanistan  :   0   non-atheist:952   1st Qu.:2012  
##  Argentina    :   0                     Median :2012  
##  Armenia      :   0                     Mean   :2012  
##  Australia    :   0                     3rd Qu.:2012  
##  Austria      :   0                     Max.   :2012  
##  (Other)      :   0
inference(usa05$response, est = "proportion", type = "ci", method = "theoretical", 
    success = "atheist")
## Single proportion -- success: atheist 
## Summary statistics:

## p_hat = 0.01 ;  n = 1002 
## Check conditions: number of successes = 10 ; number of failures = 992 
## Standard error = 0.0031 
## 95 % Confidence interval = ( 0.0038 , 0.0161 )
inference(usa12$response, est = "proportion", type = "ci", method = "theoretical", 
    success = "atheist")
## Single proportion -- success: atheist 
## Summary statistics:

## p_hat = 0.0499 ;  n = 1002 
## Check conditions: number of successes = 50 ; number of failures = 952 
## Standard error = 0.0069 
## 95 % Confidence interval = ( 0.0364 , 0.0634 )

Answer: For USA 95% confidence interval for 2005 is ( 0.0038 , 0.0161 ) and for 2012 is( 0.0364 , 0.0634 ). Clearly there is no overlap between them and this suggests that there has been a change in atheism index in the USA between 2005 and 2012.

the atheism index in the countries listed in Table 4, in how many of those countries would you expect to detect a change (at a significance level of 0.05) simply by chance?
Hint: Look in the textbook index under Type 1 error.