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46 Cards in this Set

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Statistics
the science of collecting, organizing, and interpreting data
population
the larger group we are attempting to study
Population parameters
the true numbers that describe the population abstract- we don't actually know what they are yet (estimation) we are trying to get close to these
Sample
the smaller subset of the population that we actually study, measure, survey
sample statistics
the stats that we actually measure, calculate from the sample Ex. mean, median, mode etc.
5 Steps to a Statistical Study
State goal
Decide sample size
Collect
Calculate
Conclude
Simple Random Sampling
choose items so that every sample has an equal chance of being selected (Best)
systematic sampling
simple system choosing every kth number
convenience sampling
samples that are convenient to select
stratified sampling
partition the population into at least two strata then draw a sample from each
bias
if the sample doesn't reflect the population
selection
the sample we pick reflects our bias
participation
people get to decide whether to be part of the study
observational study
researchers observe or measure characteristics but do not attempt to influence or modify them
experimental study
apply a treatment to some or all of the sample members and then look to see if it has any effects
case control study
combo of observational and experimental, we don't conduct the experiment, we just observe a natural one, and then ask questions about it. the people are already engaging in a specific behavior
control group
group in experiment that does not receive treatment
treatment group
group in experiment that does receive treatment
placebo
given to control group (no active ingredient, sugar pill)
placebo effect
when people think/believe they are getting treated so they (believe they) are getting better (sometimes actual physical improvement)
single-blind experiment
participants don't know if they are getting placebo, but experienters do
double-blind
only third party knows if there is a placebo
margin of error
an attempt to measure and describe how wrong we are if we add/subtract the MOE to a sample stat we get the confidence interval
confidence interval
where the true population parameter probably falls.
Ex. given 1.5% MOE 34% of all women drivers... Take 34+1.5 and 34-1.5 which gives you a range of 32.5 to 35.5 this range is the confidence interval
Qualitative Data
qualities, characteristics, adjectives, descriptions, ex. months, colors
Quantitative Data
Quantities, numbers, amounts
Frequency
numbers, counts (not an answer, just a count of what they said, turn tallies into frequencies)
Cumulative Frequency
add each frequency to the one before it
relative frequency
percent compared to the total number in the sample, take frequency and divide by total, n, then move decimal two places to make a percent
scatterplot
the graphical representation and test for correlation
Correlation
a number from -1 to +1 that measures how strong a relationship is between two factors
distribution
the values taken on by the variable and the frequency of these values
mean
average sum of all values divided by total number of values
median
the middle value (or average of two middle values)
mode
most common value
outlier
data value that is much higher or lower than almost all other values (can distort mean, but not median or mode)
symmetric
left half is mirror image of right half
left skewed
values are more spread out on left side, distorts mean to the left
right skewed
values are more spread out on right side, distorts mean to the right
variation
describes how widely data values are spread out about the center of distribution
Peak
the mode on a graph
Bimodal
two equal peaks
5 # summary
min
max
median
lower quartile
upper quartile
Variance
Total Sum from Deviation column, divided by n-1
Normal Distribution
Bell-Shaped Curve
Standard Deviation
a measure of how far data values are spread around the mean of a data set