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5 http://www.caffeineinformer.com/the-caffeine-database

6 1610.6508.3108.475101.2753.814009.5 303.912.88.816.7117.6103181.812.5802.813.41.99.60 10.410.69.846.70.8109.63.311.812.93.40.79.87.512.5 6.211.115.66.748.11012.52.633.333.20.36.7100.5 60.912.510.12.113.390.62.2434.83.218.16.2509.5 7.11002.1804.39.50404.251.39.410011.8 3.69.53.83.21.49.62.610.310.1104.57.16.26015.6 28.9103.63.20100.512.610.15.63.362.54050011.7 28.9106.93.210.68.80.81008.31152.92.88.94750.8 10.780235.33.26.9322.49.58.79.33.919961.614 53.5109.55.86.9109.409.503.26013.309.5 13.8109.53.2010.46.99.58.3103.58.810.228012.8 3.310 4.511.28.89.63.16.2565102.82.415 3.1159.51.89.58.812.511.97.505126.1016.7 0101003.210.98.86.29.62513.83.8128.1109.2 2.6103.16.711.410.1109.58.83.53.212.5 13.767.5 4108.83.460.859.515701.610.67.56.23.1100 1.814.710.23.4104.5118.512.59.4 55502504 3.9109.560104.514.312.58.84.718.11.46.21710 2.6152.215.616.24.58.83.211.28.13.49.5128.92.8 15 7.510469.710015.1154.2552.38.30.6 10.7151409.518.85.89.111.96.212.53.210 1.93.8 11.61031.213.13.64.59.112.56.903.211.31512.510.6 29.43.231.211.99.54.69.1156.97.550108.91283.3 13.96.71.911.26.253.124.4103.43.5101.823.51.95.9 16.79.49.5088.5010156.29013.249.56.22.8 1510.612.810010.44.42.156159.52.89.52.70 82.214.212.52505.47.5128012.512.83.89.612.52.8 35.76503.18.112.512.71010.63.4714.33.809.52.8 21.96.713.110.6011.90200.64.1253.88.922.333.3 Mg/fl oz of caffeine (n = 578…partial data shown)

7 Mg/fl oz of caffeine (n = 578) 71% have the same or less caffeine than a cup of coffee (~12 mg/fl oz). 90% have less than 3 times the amount of caffeine of coffee (~36 mg/fl oz).

8 Why do we care about outliers? 1)Should we use the average?

9 Why do we care about outliers? 1)Should we use the average? 2)We can find potentially erroneous data.

10 Why do we care about outliers? 1)Should we use the average? 2)We can find potentially erroneous data. 3)Their presence might show a meaningful (albeit unexpected) nuance:

11 Why do we care about outliers? 1)Should we use the average? 2)We can find potentially erroneous data. 3)Their presence might show a meaningful (albeit unexpected) nuance:

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