Chapter 19 Inference for one mean

2.0

Functions introduced in this chapter

rnorm

19.1 Introduction

In this chapter, we’ll learn about the Student t distribution and use it to perform a t test for a single mean.

19.1.1 Install new packages

There are no new packages used in this chapter.

19.1.2 Download the R notebook file

Check the upper-right corner in RStudio to make sure you’re in your intro_stats project. Then click on the following link to download this chapter as an R notebook file (.Rmd).

Once the file is downloaded, move it to your project folder in RStudio and open it there.

19.1.3 Restart R and run all chunks

In RStudio, select “Restart R and Run All Chunks” from the “Run” menu.

19.2 Load packages

We load the standard tidyverse and infer packages as well as the mosaic package to run some simulation. The openintro package contains the teacher data and the hsb2 data.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6      ✔ purrr   0.3.4 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.0      ✔ stringr 1.4.1 
## ✔ readr   2.1.2      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(infer)
library(mosaic)

## Registered S3 method overwritten by 'mosaic':
##   method                           from   
##   fortify.SpatialPolygonsDataFrame ggplot2
## 
## The 'mosaic' package masks several functions from core packages in order to add 
## additional features.  The original behavior of these functions should not be affected by this.
## 
## Attaching package: 'mosaic'
## 
## The following object is masked from 'package:Matrix':
## 
##     mean
## 
## The following objects are masked from 'package:infer':
## 
##     prop_test, t_test
## 
## The following objects are masked from 'package:dplyr':
## 
##     count, do, tally
## 
## The following object is masked from 'package:purrr':
## 
##     cross
## 
## The following object is masked from 'package:ggplot2':
## 
##     stat
## 
## The following objects are masked from 'package:stats':
## 
##     binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
##     quantile, sd, t.test, var
## 
## The following objects are masked from 'package:base':
## 
##     max, mean, min, prod, range, sample, sum

library(openintro)

## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata
## 
## Attaching package: 'openintro'
## 
## The following object is masked from 'package:mosaic':
## 
##     dotPlot
## 
## The following objects are masked from 'package:lattice':
## 
##     ethanol, lsegments

19.3 Simulating means

Systolic blood pressure (SBP) for women in the U.S. and Canada follows a normal distribution with a mean of 114 and a standard deviation of 14.

Suppose we gather a random sample of 4 women and measure their SBP. We can simulate doing that with the rnorm command:

set.seed(5151977)
SBP_sample <- rnorm(4, mean = 114, sd = 14)
SBP_sample

## [1]  99.75130 126.47739  99.53632 115.05247

We summarize our sample by taking the mean and standard deviation:

mean(SBP_sample)

## [1] 110.2044

sd(SBP_sample)

## [1] 13.05615

The sample mean \(\bar{y}\) = 110.2043696 is somewhat close to the true population mean \(\mu = 114\) and the sample standard deviation \(s\) = 13.0561519 is somewhat close to the true population standard deviation \(\sigma = 14\). (\(\mu\) is the Greek letter “mu” and \(\sigma\) is the Greek letter “sigma”.)

Let’s simulate lots of samples of size 4. For each sample, we calculate the sample mean.

set.seed(5151977)
sims <- do(2000) * mean(rnorm(4, mean = 114, sd = 14))
sims

##           mean
## 1    110.95524
## 2    111.06853
## 3    109.91266
## 4    113.51487
## 5    114.84292
## 6    124.12671
## 7    110.52277
## 8    122.91483
## 9    113.79958
## 10   121.52306
## 11   119.45527
## 12   130.95196
## 13   106.25140
## 14   119.48189
## 15   122.95412
## 16   111.36293
## 17   115.26561
## 18   120.00887
## 19   111.12422
## 20   125.11449
## 21   112.54356
## 22   121.05007
## 23   111.92577
## 24   112.37685
## 25   108.60242
## 26   112.14135
## 27   121.03786
## 28   102.21504
## 29   131.42457
## 30   115.75208
## 31   118.57539
## 32   107.75367
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## 34   107.48598
## 35   104.02251
## 36   110.26283
## 37   114.03591
## 38   105.89310
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## 42   102.65507
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## 1626 101.97438
## 1627 116.78661
## 1628 110.29435
## 1629 123.46893
## 1630 116.59034
## 1631 102.98659
## 1632 110.18205
## 1633 109.23006
## 1634 120.82451
## 1635 121.75575
## 1636 112.22303
## 1637 105.56302
## 1638 121.47504
## 1639 110.05367
## 1640 104.12317
## 1641 113.38777
## 1642 111.67334
## 1643 114.42323
## 1644 106.43060
## 1645 135.61538
## 1646 115.60082
## 1647 117.35365
## 1648 113.11384
## 1649 117.95815
## 1650 112.93363
## 1651 113.47685
## 1652 105.97033
## 1653 114.98154
## 1654 107.74614
## 1655 116.06752
## 1656 122.10031
## 1657 112.99138
## 1658 114.29996
## 1659 106.96841
## 1660 108.60291
## 1661 121.43145
## 1662  92.95222
## 1663 113.02822
## 1664 111.52353
## 1665 105.84010
## 1666 129.57170
## 1667 116.08035
## 1668 117.68832
## 1669 116.61900
## 1670 122.81734
## 1671 115.13204
## 1672 114.64356
## 1673 116.38536
## 1674 109.62207
## 1675 110.92888
## 1676 114.36763
## 1677 105.78363
## 1678 116.03262
## 1679 101.17360
## 1680 118.69656
## 1681 114.24099
## 1682 113.32326
## 1683 103.40502
## 1684 112.39048
## 1685 113.95558
## 1686 115.26227
## 1687 124.42804
## 1688 118.27150
## 1689 123.43397
## 1690 124.46988
## 1691 110.85936
## 1692 114.07836
## 1693 112.95339
## 1694 120.96305
## 1695 122.99706
## 1696 115.55654
## 1697 115.87079
## 1698 115.29694
## 1699 125.24085
## 1700 128.62481
## 1701 108.18660
## 1702 105.87661
## 1703 104.39514
## 1704 113.98015
## 1705 119.94162
## 1706 120.89582
## 1707 115.58448
## 1708 124.34802
## 1709 119.27451
## 1710 125.94165
## 1711 118.74313
## 1712 118.66193
## 1713 117.66496
## 1714 107.85271
## 1715 116.25929
## 1716 118.91040
## 1717 112.54798
## 1718 108.35888
## 1719 106.78994
## 1720 122.41502
## 1721 112.78309
## 1722 111.49801
## 1723 110.25142
## 1724 100.29204
## 1725 109.43258
## 1726 109.33677
## 1727 108.22743
## 1728 121.09013
## 1729 126.03049
## 1730 114.12910
## 1731 114.46407
## 1732 101.81167
## 1733 109.25527
## 1734 117.17836
## 1735 114.15739
## 1736 106.89497
## 1737 113.97003
## 1738 109.90265
## 1739 120.60851
## 1740 126.19142
## 1741 121.11972
## 1742 111.88430
## 1743 105.94220
## 1744 113.77476
## 1745 111.42531
## 1746 114.39007
## 1747 115.78462
## 1748 112.93819
## 1749 118.81692
## 1750 118.76391
## 1751 123.01901
## 1752 111.04410
## 1753 118.35484
## 1754 110.54607
## 1755 110.85959
## 1756 105.96548
## 1757 116.78229
## 1758 108.15793
## 1759 110.14765
## 1760 109.63972
## 1761 112.02199
## 1762 114.85539
## 1763 117.21206
## 1764 115.58728
## 1765  99.67584
## 1766 116.18988
## 1767 106.56255
## 1768 110.93185
## 1769 120.20929
## 1770 110.24173
## 1771 115.38537
## 1772 123.69769
## 1773 115.34699
## 1774 111.34985
## 1775 109.82229
## 1776 115.89685
## 1777 118.99048
## 1778 118.77597
## 1779 111.15591
## 1780 116.88276
## 1781 116.84949
## 1782 107.54415
## 1783 115.28064
## 1784 113.47038
## 1785 110.72918
## 1786 111.94738
## 1787 107.27141
## 1788 115.04275
## 1789  96.72293
## 1790 122.32240
## 1791 104.26958
## 1792 123.25807
## 1793 115.92358
## 1794 117.70162
## 1795 118.16755
## 1796 118.03596
## 1797 120.34519
## 1798 104.31188
## 1799 132.04806
## 1800 117.71137
## 1801 113.05951
## 1802 110.26341
## 1803 127.21428
## 1804 117.25141
## 1805 108.35096
## 1806 110.27506
## 1807 111.23149
## 1808 124.83066
## 1809 123.39050
## 1810 106.58225
## 1811 109.74921
## 1812 109.04106
## 1813 125.43409
## 1814 110.93092
## 1815 111.74767
## 1816 101.40743
## 1817 116.73829
## 1818 102.78626
## 1819 112.74032
## 1820 105.15150
## 1821 106.97115
## 1822 120.82963
## 1823 115.17882
## 1824 118.71154
## 1825 124.19609
## 1826 109.75987
## 1827 120.38832
## 1828 121.82306
## 1829 106.27523
## 1830 128.54055
## 1831 117.93971
## 1832 106.59459
## 1833 119.75123
## 1834 117.02807
## 1835 117.46441
## 1836 117.25068
## 1837 112.56719
## 1838 108.33113
## 1839 107.22700
## 1840 114.48208
## 1841 110.34761
## 1842 117.18823
## 1843 124.86804
## 1844 115.99743
## 1845 118.54041
## 1846 114.31177
## 1847 122.35911
## 1848 115.61515
## 1849 111.68315
## 1850 119.04893
## 1851 105.15279
## 1852 104.46286
## 1853 108.21831
## 1854 120.25840
## 1855 113.72293
## 1856 116.31275
## 1857 110.21878
## 1858 104.04796
## 1859 116.13271
## 1860  99.73447
## 1861 114.76161
## 1862 123.04099
## 1863 114.48397
## 1864 119.41272
## 1865 114.43066
## 1866 116.57754
## 1867 104.68885
## 1868 102.26670
## 1869 111.31379
## 1870 107.89620
## 1871 107.26937
## 1872 128.56182
## 1873 112.31984
## 1874 117.48175
## 1875 111.82601
## 1876 121.53766
## 1877 108.59204
## 1878 114.00073
## 1879 109.15453
## 1880 115.40349
## 1881 120.02438
## 1882 120.00529
## 1883 114.15522
## 1884  97.21296
## 1885 118.74600
## 1886 110.07800
## 1887 105.74195
## 1888 109.99513
## 1889 115.58094
## 1890  98.49195
## 1891 119.22469
## 1892 108.36079
## 1893 123.17149
## 1894 122.71776
## 1895 119.61528
## 1896 113.61297
## 1897 104.29065
## 1898 119.35944
## 1899 114.59634
## 1900 114.87640
## 1901 114.83493
## 1902 120.75232
## 1903 116.33686
## 1904 112.85593
## 1905 108.99668
## 1906 119.80091
## 1907 107.51762
## 1908 117.00237
## 1909 125.47799
## 1910 109.23858
## 1911  99.10170
## 1912 113.58951
## 1913 110.50543
## 1914 120.26970
## 1915 112.06393
## 1916 101.04741
## 1917 112.63951
## 1918 113.25368
## 1919 121.02941
## 1920 120.40065
## 1921 102.51873
## 1922 122.20321
## 1923 121.08449
## 1924 119.55367
## 1925 115.73619
## 1926 108.47358
## 1927 113.91919
## 1928 115.65892
## 1929 117.53470
## 1930 113.44030
## 1931 112.06709
## 1932 106.90271
## 1933 113.75108
## 1934 118.57237
## 1935 115.23998
## 1936 108.66065
## 1937 108.24943
## 1938 112.21938
## 1939 124.59338
## 1940 113.36595
## 1941 107.43284
## 1942 115.07636
## 1943 116.41288
## 1944 114.93979
## 1945 112.58356
## 1946 118.89955
## 1947 113.45179
## 1948 109.08609
## 1949 122.58892
## 1950 101.93728
## 1951 106.47563
## 1952 120.28890
## 1953 109.49638
## 1954 104.30374
## 1955 112.77269
## 1956 124.76056
## 1957 118.72269
## 1958 123.78044
## 1959 110.63524
## 1960 109.31897
## 1961 107.73594
## 1962 116.17672
## 1963 105.96558
## 1964 119.74607
## 1965 118.69882
## 1966 115.85835
## 1967 104.62583
## 1968 113.57872
## 1969 128.22431
## 1970 115.12682
## 1971 114.34633
## 1972 106.33976
## 1973 112.85725
## 1974 109.54481
## 1975 126.89872
## 1976 106.20579
## 1977 114.33387
## 1978 118.06756
## 1979 120.88291
## 1980 112.68291
## 1981 126.43337
## 1982 110.43387
## 1983 114.83281
## 1984 116.18950
## 1985 105.62630
## 1986 122.38782
## 1987 118.93003
## 1988 113.00455
## 1989 121.08291
## 1990 124.71230
## 1991 111.14368
## 1992 111.19670
## 1993 114.69397
## 1994 113.91546
## 1995 111.82721
## 1996 112.65771
## 1997 118.70725
## 1998 109.79392
## 1999 114.41826
## 2000 114.76945

Again, we see that the sample means are close to 114, but there is some variability. Naturally, not every sample is going to have an average of exactly 114. So how much variability do we expect? Let’s graph and find out. We’re going to set the x-axis manually so that we can do some comparisons later.

ggplot(sims, aes(x = mean)) +
    geom_histogram(binwidth = 1) +
    scale_x_continuous(limits = c(86, 142),
                       breaks = c(93, 100, 107, 114, 121, 128, 135))

## Warning: Removed 2 rows containing missing values (geom_bar).

Most sample means are around 114, but there is a good range of possibilities from around 93 to 135. The population standard deviation \(\sigma\) is 14, but the standard deviation in this graph is clearly much smaller than that. (A large majority of the samples are within 14 of the mean!)

With some fancy mathematics, one can show that the standard deviation of this sampling distribution is not \(\sigma\), but rather \(\sigma/\sqrt{n}\). In other words, this sampling distribution of the mean has a standard error of

\[ \frac{\sigma}{\sqrt{n}} = \frac{14}{\sqrt{4}} = 7. \]

This makes sense: as the sample size increases, we expect the sample mean to be more and more accurate, so the standard error should shrink with large sample sizes.

Let’s re-scale the y-axis to use percentages instead of counts. Then we should be able to superimpose the normal model \(N(114, 7)\) to check visually that it’s the right fit.

# Don't worry about the syntax here.
# You won't need to know how to do this on your own.
ggplot(sims, aes(x = mean)) +
    geom_histogram(aes(y = ..density..), binwidth = 1) +
    scale_x_continuous(limits = c(86, 142),
                       breaks = c(93, 100, 107, 114, 121, 128, 135)) +
    stat_function(fun = dnorm, args = list(mean = 114, sd = 7),
                  color = "red", size = 1.5)

## Warning: Removed 2 rows containing missing values (geom_bar).

Looks pretty good!

All we do now is convert everything to z scores. In other words, suppose we sample 4 individuals from a population distributed according to the normal model \(N(0, 1)\). Now the standard error of the sampling distribution is

\[ \frac{\sigma}{\sqrt{n}} = \frac{1}{\sqrt{4}} = 0.5. \]

The following code will accomplish all of this. (Don’t worry about the messy syntax. All I’m doing here is making sure that this graph looks exactly the same as the previous graph, except now centered at \(\mu = 0\) instead of \(\mu = 114\).)

# Don't worry about the syntax here.
# You won't need to know how to do this on your own.
sims_z <- data.frame(mean = scale(sims$mean, center = 114, scale = 14))
ggplot(sims_z, aes(x = mean)) +
    geom_histogram(aes(y = ..density..), binwidth = 1/14) +
    scale_x_continuous(limits = c(-2, 2),
                       breaks = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5)) +
    stat_function(fun = dnorm, args = list(mean = 0, sd = 0.5),
                  color = "red",  size = 1.5)

## Warning: Removed 2 rows containing missing values (geom_bar).

Remember that this is not the standard normal model \(N(0, 1)\). The standard deviation in the graph above is not 1, but 0.5 because that is the standard error when using samples of size 4. (\(1/\sqrt{4} = 0.5\).)

19.4 Unknown standard errors

If we want to run a hypothesis test, we will have a null hypothesis about the true value of the population mean \(\mu\). For example,

\[ H_{0}: \mu = 114 \]

Now we gather a sample and compute the sample mean, say 110.2043696. We would like to be able to compare the sample mean \(\bar{y}\) to the hypothesized value 114 using a z score:

\[ z = \frac{(\bar{y} - \mu)}{\sigma/\sqrt{n}} = \frac{(110.2 - 114)}{\sigma/\sqrt{4}}. \]

However, we have a problem: we usually don’t know the true value of \(\sigma\). In our SBP example, we do happen to know it’s 14, but we won’t know this for a general research question.

The best we can do with a sample is calculate this z score replacing the unknown \(\sigma\) with the sample standard deviation \(s\), 13.0561519. We’ll call this a “t score” instead of a “z score”:

\[ t = \frac{(\bar{y} - \mu)}{s/\sqrt{n}} = \frac{(110.2 - 114)}{13.06/\sqrt{4}} = -0.58. \]

The problem is that \(s\) is not a perfect estimate of \(\sigma\). We saw earlier that \(s\) is usually close to \(\sigma\), but \(s\) has its own sampling variability. That means that our earlier simulation in which we assumed that \(\sigma\) was known and equal to 14 was wrong for the type of situation that will arise when we run a hypothesis test. How wrong was it?

19.5 Simulating t scores

Let’s run the simulation again, but this time with the added uncertainty of using \(s\) to estimate \(\sigma\).

The first step is to write a little function of our own to compute simulated t scores. This function will take a sample of size \(n\) from the true population \(N(\mu, \sigma)\), calculate the sample mean and sample standard deviation, then compute the t score. Don’t worry: you won’t be required to do anything like this on your own.

# Don't worry about the syntax here.
# You won't need to know how to do this on your own.
sim_t <- function(n, mu, sigma) {
    sample_values <- rnorm(n, mean = mu, sd = sigma)
    y_bar <- mean(sample_values)
    s <- sd(sample_values)
    t <- (y_bar - mu)/(s / sqrt(n))
}

Now we can simulate doing this 2000 times.

set.seed(5151977)
sims_t <- do(2000) * sim_t(4, mu = 114, sigma = 14) 
sims_t

##              sim_t
## 1      1.670726734
## 2     -0.975666678
## 3     -0.278839393
## 4      0.907808022
## 5     -1.527274531
## 6     -1.717671837
## 7     -0.610956296
## 8     -0.177107883
## 9     -0.081578742
## 10    -0.150764283
## 11     0.105561464
## 12     0.989851233
## 13     0.754578374
## 14    -0.221752375
## 15    -0.569806798
## 16     1.056144154
## 17     0.709520796
## 18     1.786249608
## 19    -0.022957371
## 20    -0.479076521
## 21     2.196497891
## 22    -0.057126903
## 23     0.723176732
## 24     0.462163070
## 25     2.305842397
## 26    -0.541132956
## 27    -1.155518891
## 28     1.893602331
## 29     3.587253178
## 30    -1.329845154
## 31     1.786070559
## 32     0.205368769
## 33    -0.617185683
## 34     1.408927566
## 35     0.174600728
## 36    -0.585585461
## 37     0.975358819
## 38     0.867186495
## 39    -0.509037457
## 40     0.463270308
## 41     2.961196587
## 42     0.250917786
## 43    -0.151400364
## 44     2.379911294
## 45     0.965542692
## 46    -1.639114331
## 47    -0.187393864
## 48     0.702999822
## 49    -1.649486008
## 50     0.642256403
## 51    -0.445978914
## 52    -0.870684799
## 53    -0.506327234
## 54     0.515425890
## 55     1.188525622
## 56     1.173749591
## 57    -3.089680034
## 58     1.479209494
## 59    10.039858675
## 60    -1.865677247
## 61     0.208720956
## 62     1.698415163
## 63     0.874459927
## 64    -0.414113539
## 65    -2.079229096
## 66    -0.641514036
## 67    -0.046016401
## 68    -2.051611648
## 69    -1.116638893
## 70    -2.568290582
## 71    -3.634987999
## 72     0.131241299
## 73    -0.317803823
## 74    -1.063949859
## 75     0.004811193
## 76     4.439627383
## 77    -1.364313839
## 78     1.645804106
## 79    -0.201914744
## 80     0.504043393
## 81     1.440774874
## 82    -3.291032994
## 83    -1.551130801
## 84    -0.802710562
## 85    -4.861382113
## 86     1.016265268
## 87     1.080518333
## 88     2.980709799
## 89     4.326429336
## 90     0.458414619
## 91     2.037994906
## 92    -1.820144738
## 93     1.040068322
## 94     2.555396424
## 95    -0.478768875
## 96    -0.929751963
## 97    -0.508981112
## 98    -0.569059363
## 99    -1.094024179
## 100    0.110893966
## 101   -0.923379631
## 102    0.408635917
## 103   -0.521992962
## 104    2.636311764
## 105    0.636091866
## 106    0.859720275
## 107    1.253116033
## 108    0.874350704
## 109   -0.867757352
## 110   -1.337827858
## 111    0.156515269
## 112   -2.023417372
## 113    0.789119890
## 114    0.664206505
## 115   -5.013338827
## 116    1.080852724
## 117   -0.468189050
## 118   -0.592941304
## 119   -0.224440854
## 120    1.566295593
## 121    0.104289555
## 122   -1.197675728
## 123   -1.007030300
## 124    0.407430926
## 125   -1.942399658
## 126   -3.000766684
## 127    0.061485310
## 128   -1.592080649
## 129    1.051971725
## 130    3.007244391
## 131   -0.926063447
## 132    0.360010372
## 133   -1.154431763
## 134    0.837885024
## 135   -0.865787271
## 136   -1.185354554
## 137    0.295746913
## 138   -0.396571358
## 139    0.887971205
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## 141   -1.056473957
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## 143    2.166777070
## 144    0.009600946
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## 146   -0.445841081
## 147   -0.513983827
## 148    0.831912239
## 149    0.716585444
## 150   -0.341729523
## 151    1.959676409
## 152    0.501861848
## 153    1.419772119
## 154   -1.145028443
## 155    0.404685855
## 156    0.572805957
## 157   -1.261116341
## 158   -1.077860929
## 159   -0.340670950
## 160    3.191331484
## 161   -2.919014184
## 162    1.362479919
## 163    1.326437044
## 164   -0.619316503
## 165   -1.330164481
## 166    0.114571544
## 167    0.275918212
## 168   -1.609972483
## 169    0.746043178
## 170    0.571191844
## 171    1.155595866
## 172    0.134574629
## 173   -1.218916492
## 174   -1.492947751
## 175    1.012713541
## 176   -0.651309215
## 177   -2.690012483
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## 179   -0.709852732
## 180    1.127924885
## 181    2.690832381
## 182    1.716396925
## 183   -0.697362354
## 184   -0.961945375
## 185    0.746108381
## 186   -1.524226171
## 187   -0.458618707
## 188   -0.055254402
## 189    1.020115666
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## 191    0.979239006
## 192    0.785251827
## 193   -0.178483558
## 194   -1.244265037
## 195    0.744906482
## 196   -0.491305065
## 197   -0.345225608
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## 200    0.567650649
## 201    0.285171950
## 202   -0.912431467
## 203   -0.016306668
## 204   -0.018041076
## 205    0.864570995
## 206    1.856671982
## 207    0.481038270
## 208   -1.469329052
## 209    2.623871232
## 210   -0.712124175
## 211    0.392677868
## 212   -0.960771180
## 213    1.503009840
## 214   -1.308729342
## 215   -0.714134598
## 216    0.910092338
## 217    0.687880279
## 218   -0.706690653
## 219    1.039393080
## 220    1.285188816
## 221    2.082287808
## 222    0.065838057
## 223    1.905921689
## 224    1.228140674
## 225   -0.765591982
## 226    0.605332968
## 227   -0.017615429
## 228   -0.220003147
## 229   -0.921723662
## 230   -1.408301607
## 231    0.307375781
## 232   -0.384728667
## 233   -4.815204952
## 234    0.153630251
## 235   -0.544127519
## 236   -0.012780210
## 237    0.143751438
## 238    1.320877365
## 239   -1.291725993
## 240   -0.482246881
## 241    0.752661778
## 242    0.393190471
## 243    1.179327701
## 244    0.393345460
## 245   -3.793928233
## 246    5.181415482
## 247    0.564651863
## 248   -1.295222322
## 249   -1.416412176
## 250    0.491626455
## 251   -3.145790254
## 252    0.254944191
## 253    2.515832119
## 254    0.820769536
## 255    0.645464631
## 256   -0.270108112
## 257    1.810842034
## 258    1.074959231
## 259    2.627121628
## 260    1.387446754
## 261    1.645532448
## 262   -0.384565059
## 263    5.407605220
## 264   -0.037234681
## 265   -3.045039779
## 266    0.226437021
## 267   -0.146152727
## 268    1.122665692
## 269   -0.757175673
## 270    0.183402023
## 271    0.696221348
## 272    1.020714292
## 273   -0.042622579
## 274    2.912200674
## 275    0.002357622
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## 277    0.228627097
## 278    0.104690123
## 279    0.661475603
## 280   -0.506233167
## 281   -1.170819473
## 282    0.225067302
## 283   -0.286442271
## 284    1.034292157
## 285    0.968956715
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## 1999  -1.605398619
## 2000   0.409543307

Let’s plot our simulated t scores alongside a normal distribution.

# Don't worry about the syntax here.
# You won't need to know how to do this on your own.
ggplot(sims_t, aes(x = sim_t)) +
    geom_histogram(aes(y = ..density..), binwidth = 0.25) +
    scale_x_continuous(limits = c(-5, 5),
                       breaks = c(-4, -3, -2, -1, 0, 1, 2, 3, 4)) +
    stat_function(fun = dnorm, args = list(mean = 0, sd = 1),
                  color = "red", size = 1.5)

## Warning: Removed 19 rows containing non-finite values (stat_bin).

## Warning: Removed 2 rows containing missing values (geom_bar).

These t scores are somewhat close to the normal model we had when we knew \(\sigma\), but the fit doesn’t look quite right. The peak of the simulated values isn’t quite high enough, and the tails seem to spill out over the much thinner tails of the normal model.

William Gosset figured this all out in the early 20th century. While working for the Guinness brewery in Dublin, Ireland, he started noticing that his quality control tests (using very small sample sizes) didn’t yield statistical results consistent with the normal models that were universally used at the time. At the encouragement of the company, which saw his work as a potential source of cost savings, he took some time off to study and consult with other statisticians. As a result, he found a new function that is similar to a normal distribution but is more spread out. This new function accounts for the extra variability one gets when using the sample standard deviation \(s\) as an estimate for the true population standard deviation \(\sigma\). Guinness considered the result a “trade secret”, so they wouldn’t allow Gosset to publish under his own name. But they did permit him to publish his findings under the pseudonym “Student”. He used data sets unrelated to brewing and submitted his work to the top statistical journal of the time.

The new function Gosset discovered became known as the Student t distribution. He realized that the spread of the t distribution depends on the sample size. This makes sense: the accuracy of \(s\) will be greater when we have a larger sample. In fact, for large enough samples, the t distribution is very close to a normal model.

Gosset used the term degrees of freedom to describe how the sample size influences the spread of the t distribution. It’s somewhat mathematical and technical, so suffice it to say here that the number of degrees of freedom is simply the sample size minus 1:

\[ df = n - 1. \]

So is the t model correct for our simulated t scores? Our sample size was 4, so we should use a t model with 3 degrees of freedom. Let’s plot it in green on top of our previous graph and see:

# Don't worry about the syntax here.
# You won't need to know how to do this on your own.
ggplot(sims_t, aes(x = sim_t)) +
    geom_histogram(aes(y = ..density..), binwidth = 0.25) +
    scale_x_continuous(limits = c(-5, 5),
                       breaks = c(-4, -3, -2, -1, 0, 1, 2, 3, 4)) +
    stat_function(fun = dnorm, args = list(mean = 0, sd = 1),
                  color = "red", size = 1.5) +
    stat_function(fun = dt, args = list(df = 3),
                  color = "green", size = 1.5)

## Warning: Removed 19 rows containing non-finite values (stat_bin).

## Warning: Removed 2 rows containing missing values (geom_bar).

The green curve fits the simulated values much better.

19.6 Inference for one mean

When we have a single numerical variable, we can ask if the sample mean is consistent or not with a null hypothesis. We will use a t model for our sampling distribution model as long as certain conditions are met.

One of the assumptions we made in the simulation above was that the true population was normally distributed. In general, we have no way of knowing if this is true. So instead we check the nearly normal condition: if a histogram or QQ plot of our data shows that the data is nearly normal, then there is a reasonable assumption that the whole population is shaped the same way.

If our sample size is large enough, the central limit theorem tells us that the sampling distribution gets closer and closer to a normal model. Therefore, we’ll use a rule of thumb that says that if the sample size is greater than 30, we won’t worry too much about any deviations from normality in the data.

The number 30 is somewhat arbitrary. If the sample size is 25 and a histogram shows only a little skewness, we’re probably okay. But if the sample size is 10, we need for the data to be very normal to justify using the t model. The irony, of course, is that small sample sizes are the hardest to check for normality. We’ll have to use our best judgment.

19.7 Outliers

We also need to be on the lookout for outliers. We’ve seen before that outliers can have a huge effect on means and standard deviations, especially when sample sizes are small. Whenever we find an outlier, we need to investigate.

Some outliers are mistakes. Perhaps someone entered data incorrectly into the computer. When it’s clear that outliers are data entry errors, we are free to either correct them (if we know what error was made) or delete them from our data completely.

Some outliers are not necessarily mistakes, but should be excluded for other reasons. For example, if we are studying the weight of birds and we have sampled a bunch of hummingbirds and one emu, the emu’s weight will appear as an outlier. It’s not that its weight is “wrong”, but it clearly doesn’t belong in the analysis.

In general, though, outliers are real data that just happen to be unusual. It’s not ethical simply to throw away such data points because they are inconvenient. (We only do so in very narrow and well-justified circumstances like the emu.) The best policy to follow when faced with such outliers is to run inference twice—once with the outlier included, and once with the outlier excluded. If, when running a hypothesis test, the conclusion is the same either way, then the outlier wasn’t all that influential, so we leave it in. If, when computing a confidence interval, the endpoints don’t change a lot either way, then we leave the outlier in. However, when conclusions or intervals are dramatically different depending on whether the outlier was in or out, then we have no choice but to state that honestly.

19.8 Research question

The teacher data from the openintro package contains information on 71 teachers employed by the St. Louis Public School in Michigan. According to Google, the average teacher salary in Michigan was $63,024 in 2010. So does this data suggest that the teachers in the St. Louis region of Michigan are paid differently than teachers in other parts of Michigan?

Let’s walk through the rubric.

19.9 Exploratory data analysis

19.9.1 Use data documentation (help files, code books, Google, etc.) to determine as much as possible about the data provenance and structure.

You should type ?teacher at the Console to read the help file. Unfortunately, the help file does not give us a lot of information about how the data was collected. The only source listed is a website that no longer contains this data set. Besides, that website is just an open repository for data, so it’s not clear that the site would have contained any additional information about the provenance of the data. We will have to assume that the data was collected accurately.

Here is the data set:

teacher

## # A tibble: 71 × 8
##    id    degree fte   years  base  fica retirement  total
##  * <fct> <fct>  <fct> <dbl> <int> <dbl>      <dbl>  <dbl>
##  1 01    BA     1       5   45388 3472.      7689. 56549.
##  2 02    MA     1      15   60649 4640.     10274. 75563.
##  3 03    MA     1      16   60649 4640.     10274. 75563.
##  4 04    BA     1      10   54466 4167.      9227. 67859.
##  5 05    BA     1      26   65360 5000.     11072. 81432.
##  6 06    BA     1      28.5 65360 5000.     11072. 81432.
##  7 07    BA     1      12   58097 4444.      9842. 72383.
##  8 08    MA     1      32   68230 5220.     11558. 85008.
##  9 09    BA     1      25   65360 5000.     11072. 81432.
## 10 11    BA     1      12   58097 4444.      9842. 72383.
## # … with 61 more rows

glimpse(teacher)

## Rows: 71
## Columns: 8
## $ id         <fct> 01, 02, 03, 04, 05, 06, 07, 08, 09, 11, 12, 13, 14, 15, 16,…
## $ degree     <fct> BA, MA, MA, BA, BA, BA, BA, MA, BA, BA, BA, BA, BA, BA, MA,…
## $ fte        <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ years      <dbl> 5.0, 15.0, 16.0, 10.0, 26.0, 28.5, 12.0, 32.0, 25.0, 12.0, …
## $ base       <int> 45388, 60649, 60649, 54466, 65360, 65360, 58097, 68230, 653…
## $ fica       <dbl> 3472.18, 4639.65, 4639.65, 4166.65, 5000.04, 5000.04, 4444.…
## $ retirement <dbl> 7688.73, 10273.94, 10273.94, 9226.54, 11071.98, 11071.98, 9…
## $ total      <dbl> 56548.91, 75562.59, 75562.59, 67859.19, 81432.02, 81432.02,…

Since total is a numerical variable, we can use the summary function to produce the five-number summary. (The function also reports the mean.)

summary(teacher$total)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   24793   63758   74647   70289   81432   85008

19.9.2 Prepare the data for analysis.

Not necessary here, but see the next section to find out what we do when we discover an outlier.

19.9.3 Make tables or plots to explore the data visually.

Here is a histogram.

ggplot(teacher, aes(x = total)) +
    geom_histogram(binwidth = 5000, boundary = 60000)

And here is a QQ plot.

ggplot(teacher, aes(sample = total)) +
    geom_qq() +
    geom_qq_line()

This distribution is quite skewed to the left. Of even more concern is the extreme outlier on the left.

With any outlier, we need to investigate.

Exercise 1

Let’s sort the data by total (ascending) using the arrange command.

teacher %>%
    arrange(total)

## # A tibble: 71 × 8
##    id    degree fte   years  base  fica retirement  total
##    <fct> <fct>  <fct> <dbl> <int> <dbl>      <dbl>  <dbl>
##  1 37    MA     0.5       1 19900 1522.      3371. 24793.
##  2 12    BA     1         0 35427 2710.      6001. 44138.
##  3 57    BA     1         0 35427 2710.      6001. 44138.
##  4 41    BA     1         1 37199 2846.      6302. 46346.
##  5 69    BA     1         2 38968 2981.      6601. 48550.
##  6 48    BA     1         3 40739 3117.      6901. 50757.
##  7 54    BA     1         3 40739 3117.      6901. 50757.
##  8 38    MA     1         2 41695 3190.      7063. 51948.
##  9 15    BA     1         4 43575 3333.      7382. 54290.
## 10 39    MA     1         3 43593 3335.      7385. 54313.
## # … with 61 more rows

Can you figure out why the person with the lowest total salary is different from all the other teachers?

Please write up your answer here.

---

Based on your answer to the above exercise, hopefully it’s clear that this is an outlier for which we can easily justify exclusion. We can use the filter command to get only the rows we want. There are lots of ways to do this, but it’s easy enough to grab only salaries above $30,000. (There’s only one salary below $30,000, so that outlier will be excluded.)

CAUTION: If you are copying and pasting from this example to use for another research question, the following code chuck is specific to this research question and not applicable in other contexts.

teacher2 <- teacher %>%
    filter(total > 30000)

Check to make sure this had the desired effect:

summary(teacher2$total)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   44138   63758   74647   70939   81432   85008

Notice how the min is no longer $24,793.41.

Here are the new plots:

ggplot(teacher2, aes(x = total)) +
    geom_histogram(binwidth = 5000, boundary = 60000)

ggplot(teacher2, aes(sample = total)) +
    geom_qq() +
    geom_qq_line()

The left skew is still present, but we have removed the outlier.

19.10 Hypotheses

19.10.1 Identify the sample (or samples) and a reasonable population (or populations) of interest.

The sample consists of 70 teachers employed by the St. Louis Public School in Michigan. We are using these 70 teachers as a hopefully representative sample of all teachers in that region of Michigan.

19.10.2 Express the null and alternative hypotheses as contextually meaningful full sentences.

\(H_{0}:\) Teachers in the St. Louis region earn $63,024 on average. (In other words, these teachers are the same as the teachers anywhere else in Michigan.)

\(H_{A}:\) Teachers in the St. Louis region do not earn $63,024 on average. (In other words, these teachers are not the same as the teachers anywhere else in Michigan.)

19.10.3 Express the null and alternative hypotheses in symbols (when possible).

\(H_0: \mu = 63024\)

\(H_A: \mu \neq 63024\)

19.11 Model

19.11.1 Identify the sampling distribution model.

We will use a t model with 69 degrees of freedom.

Commentary: The original teacher data had 71 observations. The teacher2 data has only 70 observations because we removed an outlier. Therefore \(n = 70\) and thus \(df = n - 1 = 69\).

19.11.2 Check the relevant conditions to ensure that model assumptions are met.

• Random
  • We know this isn’t a random sample. We’re not sure if this school is representative of other schools in the region, so we’ll proceed with caution.
• 10%
  • This is also suspect, as it’s not clear that there are 700 teachers in the region. One way to look at it is this: if there are 10 or more schools in the region, and all the school are about the size of the St. Louis Public School under consideration, then we should be okay.
• Nearly Normal
  • For this, we note that the sample size is much larger than 30, so we should be okay, even with the skewness in the data.

19.12 Mechanics

19.12.1 Compute the test statistic.

total_mean <- teacher2 %>%
  specify(response = total) %>%
  calculate(stat = "mean")
total_mean

## Response: total (numeric)
## # A tibble: 1 × 1
##     stat
##    <dbl>
## 1 70939.

total_t <- teacher2 %>%
  specify(response = total) %>%
  hypothesize(null = "point", mu = 63024) %>%
  calculate(stat = "t")
total_t

## Response: total (numeric)
## Null Hypothesis: point
## # A tibble: 1 × 1
##    stat
##   <dbl>
## 1  5.89

19.12.2 Report the test statistic in context (when possible).

The sample mean is $70938.5725714.

The t score is 5.886253. The mean teacher salary in our sample is almost 6 standard errors to the right of the null value.

19.12.3 Plot the null distribution.

total_test <- teacher2 %>%
  specify(response = total) %>%
  assume("t")
total_test

## A T distribution with 69 degrees of freedom.

total_test %>%
  visualize() +
  shade_p_value(obs_stat = total_t, direction = "two-sided")

Commentary: Although we are conducting a two-sided test, the area in the tails is so small that it can’t really be seen in the picture above.

19.12.4 Calculate the P-value.

total_test_p <- total_test %>%
  get_p_value(obs_stat = total_t, direction = "two-sided")
total_test_p

## # A tibble: 1 × 1
##       p_value
##         <dbl>
## 1 0.000000129

19.12.5 Interpret the P-value as a probability given the null.

\(P < 0.001\). If teachers in the St. Louis region truly earned $63,024 on average, there would be only a 0.0000129% chance of seeing data at least as extreme as what we saw.

Commentary: When the P-value is this small, remember that it is traditional to report simply \(P < 0.001\).

19.13 Conclusion

19.13.1 State the statistical conclusion.

We reject the null hypothesis.

19.13.2 State (but do not overstate) a contextually meaningful conclusion.

There is sufficient evidence that teachers in the St. Louis region do not earn $63,024 on average.

19.13.3 Express reservations or uncertainty about the generalizability of the conclusion.

Because we do not know how this data was collected (was it every teacher in this region? was it a sample of some of the teachers? was it a representative sample?), we do not know if we can generalize it to all teachers in the region. Also, the data set was from 2010, so we know that this data cannot be applied to teachers in St. Louis, Michigan now.

19.13.4 Identify the possibility of either a Type I or Type II error and state what making such an error means in the context of the hypotheses.

If we’ve made a Type I error, then the truth is that teachers in this region do make around $63,024 on average, but our sample was way off.

19.14 Confidence interval

19.14.1 Check the relevant conditions to ensure that model assumptions are met.

All the conditions have been checked already.

19.14.2 Calculate and graph the confidence interval.

total_ci <- total_test %>%
  get_confidence_interval(point_estimate = total_mean, level = 0.95)
total_ci

## # A tibble: 1 × 2
##   lower_ci upper_ci
##      <dbl>    <dbl>
## 1   68256.   73621.

total_test %>%
  visualize() +
  shade_confidence_interval(endpoints = total_ci)

19.14.3 State (but do not overstate) a contextually meaningful interpretation.

We are 95% confident that the true mean salary for teachers in the St. Louis region is captured in the interval (68256.2, 73620.95).

Commentary: As these are dollar amounts, it makes sense to round them to two decimal places. Even then, R is finicky and sometimes it will not respect your wishes.)

19.14.4 If running a two-sided test, explain how the confidence interval reinforces the conclusion of the hypothesis test.

Since $63,024 is not contained in the confidence interval, it is not a plausible value for the mean teacher salary in the St Louis region of Michigan.

19.14.5 When comparing two groups, comment on the effect size and the practical significance of the result.

We are not comparing two groups.

19.15 Your turn

In the High School and Beyond survey (the hsb2 data set from the openintro package), among the many scores that are recorded are standardized math scores. Suppose that these scores are normalized so that a score of 50 represents some kind of international average. (This is not really true. I had to make something up here to give you a baseline number with which to work.) The question is, then, are American students different from this international baseline?

The rubric outline is reproduced below. You may refer to the worked example above and modify it accordingly. Remember to strip out all the commentary. That is just exposition for your benefit in understanding the steps, but is not meant to form part of the formal inference process.

Another word of warning: the copy/paste process is not a substitute for your brain. You will often need to modify more than just the names of the data frames and variables to adapt the worked examples to your own work. Do not blindly copy and paste code without understanding what it does. And you should never copy and paste text. All the sentences and paragraphs you write are expressions of your own analysis. They must reflect your own understanding of the inferential process.

Also, so that your answers here don’t mess up the code chunks above, use new variable names everywhere.

Exploratory data analysis

Use data documentation (help files, code books, Google, etc.) to determine as much as possible about the data provenance and structure.

Please write up your answer here

# Add code here to print the data

# Add code here to glimpse the variables

Prepare the data for analysis. [Not always necessary.]

# Add code here to prepare the data for analysis.

Make tables or plots to explore the data visually.

# Add code here to make tables or plots.

Hypotheses

Identify the sample (or samples) and a reasonable population (or populations) of interest.

Please write up your answer here.

Express the null and alternative hypotheses as contextually meaningful full sentences.

\(H_{0}:\) Null hypothesis goes here.

\(H_{A}:\) Alternative hypothesis goes here.

Express the null and alternative hypotheses in symbols (when possible).

\(H_{0}: math\)

\(H_{A}: math\)

Model

Identify the sampling distribution model.

Please write up your answer here.

Check the relevant conditions to ensure that model assumptions are met.

Please write up your answer here. (Some conditions may require R code as well.)

Mechanics

Compute the test statistic.

# Add code here to compute the test statistic.

Report the test statistic in context (when possible).

Please write up your answer here.

Plot the null distribution.

# IF CONDUCTING A SIMULATION...
set.seed(1)
# Add code here to simulate the null distribution.

# Add code here to plot the null distribution.

Calculate the P-value.

# Add code here to calculate the P-value.

Interpret the P-value as a probability given the null.

Please write up your answer here.

Conclusion

State the statistical conclusion.

Please write up your answer here. {-}

State (but do not overstate) a contextually meaningful conclusion.

Please write up your answer here.

Express reservations or uncertainty about the generalizability of the conclusion.

Please write up your answer here.

Identify the possibility of either a Type I or Type II error and state what making such an error means in the context of the hypotheses.

Please write up your answer here.

Confidence interval

Check the relevant conditions to ensure that model assumptions are met.

Please write up your answer here. (Some conditions may require R code as well.)

Calculate and graph the confidence interval.

# Add code here to calculate the confidence interval.

# Add code here to graph the confidence interval.

State (but do not overstate) a contextually meaningful interpretation.

Please write up your answer here.

If running a two-sided test, explain how the confidence interval reinforces the conclusion of the hypothesis test. [Not always applicable.]

Please write up your answer here.

When comparing two groups, comment on the effect size and the practical significance of the result. [Not always applicable.]

Please write up your answer here.

19.16 Additional exercises

After running inference above, answer the following questions:

Exercise 2

Even though the result was statistically significant, do you think the result is practically significant? By this, I mean, are scores for American students so vastly different than 50? Do we have a lot of reason to brag about American scores based on your analysis?

Please write up your answer here.

Exercise 3

What makes it possible for a small effect like this to be statistically significant even if it’s not practically very different from 50? In other words, what has to be true of data to detect small but statistically significant effects?

Please write up your answer here.

19.17 Conclusion

When working with numerical data, we have to estimate a mean and a standard deviation. The extra variability in estimating both gives rise to a sampling distribution model with thicker tails called the Student t distribution. Using this distribution gives us a way to calculate P-values and confidence intervals that take this variation into account.

19.17.1 Preparing and submitting your assignment

1. From the “Run” menu, select “Restart R and Run All Chunks”.
2. Deal with any code errors that crop up. Repeat steps 1–-2 until there are no more code errors.
3. Spell check your document by clicking the icon with “ABC” and a check mark.
4. Hit the “Preview” button one last time to generate the final draft of the .nb.html file.
5. Proofread the HTML file carefully. If there are errors, go back and fix them, then repeat steps 1–5 again.

If you have completed this chapter as part of a statistics course, follow the directions you receive from your professor to submit your assignment.