Quiz 02_Review Flashcards
alleged true extent of variation in the sample
standard deviation
mathematically equal to the product of the sample size and the sample mean
total score
the characteristic in a dataset that is used to estimate the standard error of the kurtosis
kurtosis
this is what we use as a divisor to address estimation biases whenever we compute for a central statistic in a sample
degrees of freedom
tells us the number of times the amount of variation observed from an individual is when compared to the typical amount of variation in the dataset
squared standardize—
amount of variation from the mean that each participant in a sample is expected to show
variance
the total amount of variation in the dataset
sum of squares
number of units that the score of an individual is an overestimation or an underestimation of the mean
deviation from the mean
the expected score of an individual on a property given that he/she is a homogenous sample
mean
the attribute in an ordinal dataset where 50% of the observations are below it
median
tells us the typical amount of symmetry in a dataset
skewness
squared mean-centered observations
squared deviation from the mean/squared errors
peakedness in the distrubution of a dataset
kurtosis
the nominal attribute that has the highest frequency of occurrence in the dataset
mode
tells us the number of standard deviations a certain score is above or below the center of distribution
standardized score (z-score)
typical extent of variation in a dataset
standard deviation
theoretically estimated true amount of variation in a dataset
variance
number of units of discrepancy between the empirical and the theoretical score of each individual in a sample
deviation from the mean/error
number of observations allowed to vary when estimating a value of a sample statistic
degrees of freedom
gravitational center of the dataset
mean
the product of the (n-1) and the variance
sum of squares
amount of variation from the mean observed from an individual in a sample
squared deviation from the mean/squared errors
actual attributes used to describe the property of an individual
raw score
an aggregation of all the observed attributes in a data set
total score
number of attributes actually observed in the sample
sample size
the height of a distribution
kurtosis
the number of observations in the dataset that is not equal to the mean
squared standardized
tells number of times the difference between an observation and the sample mean is to the typical difference that one would naturally observe in the dataset
standardized score (z-score)
average degree/amount of how much the first half of a sample distribution is a mirror image of the other half
skewness
the middle score in a non-normal distribution
median
