Every 4 babies there will be 2 in each of the study

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every 4 babies, there will be 2 in each of the study groups) Histograms show potential outliers (i.e., any observation that doesn’t fit with the data) Cumulative frequency polygons show approximate percentile quartiles; Q 1 = 25th, Median = 50th, Q 3 = 75th, IQR = Q 3 - Sensitivity: + diagnostic; people who have the disease and are correctly diagnosed sensitivity = false negative = false positive Specificity : - diagnostic; correctly identified as not having the disease specificity = false positive = false negative False positive : healthy people misdiagnosed as having the disease False negative : people with disease misdiagnosed as being healthy Measures of central tendency 1) Arithmetic mean: x ̄ = sum of obs./# of obs.; meaningless if skewed 2) Median - ROBUST; use for skewed data 3) Geometric mean - calculate arithmetic mean of numbers after log transformation, then untransform by exponentiating with base of log; use for right-skewed distribution! 4) Mode - has most meaning with categorical data; can have multiple 5) Midrange (“Quick and dirty”): (smallest obs + largest obs) / 2 Measures of variability Average distance from the mean = 0 1) Mean absolute deviation (MAD): average absolute distance from mean 2) Variance: almost average squared deviation from the mean 3) Standard deviation: root of almost average squared deviation from mean; ASSUMPTION : data is normally distributed +/- 1sd = ~68%; 2sd = ~95%; 3sd = ~99.7%; normal range = 2sd 4) IQR: Q 3 - Q 1 ; use for skewed data 5) Standard deviation with range (“Quick and dirty”): range / 4 Sample statistics : any number calculated from sample data; n = sample size while N = population size! Population parameters : number calculated from population data, are fixed for that population, but unknown in statistical problems - Arithmetic mean = μ - Standard deviation = σ Classical Statistical Inferences (Frequentist) Mean experimental result: mean of x ̄ = μ (NOT x ̄ = μ); x ̄ is unbiased estimate of μ Standard deviation of sampling distribution of x ̄ = standard error of the mean (SEM) = σ / n; s = approximate estimate of σ → Shape of sampling distribution of x ̄ - Central Limit Theorem: data is normally distributed (true most of the time) Symmetric distribution : median = mean Right-skewed distribution : median < mean; right tail is longer; most data values are small Left-skewed distribution : median > mean; left tail is longer; some natural boundary exists on the right (e.g., test scores) Normal distribution : aka Gaussian curve, bell-shaped curve - Adult height for males, hemoglobin levels, hematocrit levels Log normal distribution : log transformation needed to get normal distribution; e.g., weight, cholesterol levels, income, housing prices Exponential “” : aka survival distribution (e.g., life expectancy)

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