2 years ago

May 15, 2015

## Z-Scores

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations) that an observation is away from the mean of the population. A closely related top is the 68-95-99.7 rule which tells us that (approximately) 68% of a normally distributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3.

Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below. They further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of a finding can be said to be $3 \sigma$, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

## Calculate the z-score of your height

Male Female

Assumed standard deviation: inches

 Ft: 3 4 5 6 7 8 Inches: 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5

 z-score: percentile:

Using the interface above, select your gender and enter your height, and your z-score will be calculated. Additionally, this page reports the percentile - the percentage of the population which is shorter than you.

\n\nMale\nFemale\n\n

\n\nAssumed standard deviation:\n inches\n\n

\n\n\n\t\n\t\t\n\t\t\n\t\n
 Ft:\n\t\t\t\n\t\t\t\t3\n\t\t\t\t4\n\t\t\t\t5\n\t\t\t\t6\n\t\t\t\t7\n\t\t\t\t8\n\t\t\t\n\t\t Inches:\n\t\t\t\n\t\t\t\t0\n\t\t\t\t0.5\n\t\t\t\t1\n\t\t\t\t1.5\n\t\t\t\t2\n\t\t\t\t2.5\n\t\t\t\t3\n\t\t\t\t3.5\n\t\t\t\t4\n\t\t\t\t4.5\n\t\t\t\t5\n\t\t\t\t5.5\n\t\t\t\t6\n\t\t\t\t6.5\n\t\t\t\t7\n\t\t\t\t7.5\n\t\t\t\t8\n\t\t\t\t8.5\n\t\t\t\t9\n\t\t\t\t9.5\n\t\t\t\t10\n\t\t\t\t10.5\n\t\t\t\t11\n\t\t\t\t11.5\n\t\t\t\n\t\t
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 z-score: percentile:
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## Z-Scores

\n\n

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations)\nthat an observation is away from the mean of the population. A closely related top is the\n68-95-99.7 rule which tells us that (approximately) 68% of a normally\ndistributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3.

\n\n

Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below.\nThey further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of\na finding can be said to be $3 \\sigma$, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

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## Calculate the z-score of your height

\n\n\n\nMale\nFemale\n\n

\n\nAssumed standard deviation:\n inches\n\n

\n\n\n\t\n\t\t\n\t\t\n\t\n
 Ft:\n\t\t\t\n\t\t\t\t3\n\t\t\t\t4\n\t\t\t\t5\n\t\t\t\t6\n\t\t\t\t7\n\t\t\t\t8\n\t\t\t\n\t\t Inches:\n\t\t\t\n\t\t\t\t0\n\t\t\t\t0.5\n\t\t\t\t1\n\t\t\t\t1.5\n\t\t\t\t2\n\t\t\t\t2.5\n\t\t\t\t3\n\t\t\t\t3.5\n\t\t\t\t4\n\t\t\t\t4.5\n\t\t\t\t5\n\t\t\t\t5.5\n\t\t\t\t6\n\t\t\t\t6.5\n\t\t\t\t7\n\t\t\t\t7.5\n\t\t\t\t8\n\t\t\t\t8.5\n\t\t\t\t9\n\t\t\t\t9.5\n\t\t\t\t10\n\t\t\t\t10.5\n\t\t\t\t11\n\t\t\t\t11.5\n\t\t\t\n\t\t
\n\n
\n\n\n\t\n\t\t\n\t\t\n\t\n\t\n\t\t\n\t\t\n\t\n
 z-score: percentile:
\n\n
\n\n

\n\n

This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations) that an observation is away from the mean of the population. A closely related top is the 68-95-99.7 rule which tells us that (approximately) 68% of a normally distributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3.

\r\n

Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-score for height, you can do so below. They further discuss how a z-score can also describe the likelihood that some statistical result is due to chance. Thus, if the significance of a finding can be said to be 3σ, that means that it's 99.7% likely not due to chance, or only 0.3% likely to be due to chance.

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Thanks to Periscope Data for sponsoring this week's episode of Data Skeptic.

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