2 years ago
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.
Ft: | Inches: |
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.
Ft:\n\t\t\t\n\t\t | \n\t\tInches:\n\t\t\t\n\t\t | \n\t
z-score: | \n\t\t\n\t |
percentile: | \n\t\t\n\t |
Using the interface above, select your gender and enter your height, and your z-score will be calculated.\nAdditionally, this page reports the percentile - the percentage of the population which is shorter than you.
\n\nThis 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\nKyle 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.
\n\nFt:\n\t\t\t\n\t\t | \n\t\tInches:\n\t\t\t\n\t\t | \n\t
z-score: | \n\t\t\n\t |
percentile: | \n\t\t\n\t |
Using the interface above, select your gender and enter your height, and your z-score will be calculated.\nAdditionally, this page reports the percentile - the percentage of the population which is shorter than you.
\n\nThis 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\nKyle 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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