Level 9
Level 10

Basic Statistical Concepts


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statistical thinking
rational and critical processing of data to draw reliable conclusions
statistical inference
estimate or prediction about a population based on data collected from a sample
resource constraints
statistical data collection is costly, limiting available size, scope, and quality of data
bell curve
normal distribution
deviation
difference between the value of a data point and the mean value for the set
median
the middle number in a sorted list of numbers
weighted mean
calculation of the mean where data points are not treated equally
range
measure of spread (variation) in a data set
mode
a hump (local high point) in the shape of a variable's distribution
distribution
states the relative frequency of each possible value of a variable
standard deviation
a commonly used measure of variability in a data set: typical deviation from the mean
outlier
a number (data point) that strongly deviates from the rest (z-score in excess of 3 or -3)
percentile
a ranking method based on breaking up a data set into 100 equal parts
relative frequency
how often something occurs (frequency per number of observations)
correlation coefficient
a statistic (r) expressing the strength and direction of the relationship between two variables (values: -1 to 1)
statistical bias
a systematic error affecting your results in some subset of sample or data
non-response bias
where certain subgroups are under-represented because of low response and participation rates
scatter plot
a graphed cluster of dots each representing values of two variables
bar graph
graph with vertical bars for each category
pie chart
graph representing values as sections of a circle
discrete values
where only several values are possible
continuous values
where many values are possible
linear regression
finding the line of best fit to a set of points
least squares
the most common method for linear regression
Type 1 error
false positive: a correlation assumed where absent
Type 2 error
false negative: correlation ignored though real
z-score
how many standard deviations a data value is from the mean
standardizing
converting values to remove specific units and make sets comparable
context
information about how, where, where etc. the data was collected
law of large numbers
as sample size increases, relative frequencies approach actual probability value
blinding
withholding of information whether or not a participant is allocated to the study group or the control group
distribution
states the relative frequency of each possible value of a variable
p-value
how likely is it that your data are random noise that looks like a pattern
significance
the p-value is less than or equal to alpha
alpha level
significance level: how likely is it that your data shows something that's actually real
confidence interval
how likely it is that a parameter value in the sample reflects actual parameter value in the population