Week 14 Flashcards
(38 cards)
population parameter
a number that describes something about an entire group or population.
ex: % of all adult Americans who
have experienced some form of
data theft or fraud
Sample statistic
a smaller, manageable version of a larger group
inferential analysis
Statistical analyses used to reach conclusions
that extend beyond the immediate data
alone
sampling distribution
a statistic is the probability
distribution for the possible values of the statistic
Central limit theorem
Given random sampling, if
the sample size n is large, the sampling distribution of
the sample mean is approximately a normal
distribution
point estimate
A single statistic value that is the “best guess”
for the parameter value
Interval estimate
An interval of numbers around the point
estimate, that (we are confident to a certain level) contains the
parameter value. Called a confidence interval
confidence interval
is an interval of numbers believed to contain the
parameter value
significance testing
uses data to summarize evidence about
a hypothesis by comparing sample estimates of parameters to
values predicted by the hypothesis
assumption
randomization
quantitative variable
Null hypothesis
a statement that parameter(s) take
specific value(s). Usually “no effect/no change” statement
alternative hypothesis
states that parameter value(s)
falls in some alternative range of values
test statistic
compares data to what null hypo. H0 predicts,
often by finding the number of standard errors between
sample point estimate and H0 value of parameter
P value
the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect
Conclusion
report and interpret what the P-value tells us about
the question motivating the test
T test
Assesses whether the means of two groups (for example, the
treatment and control groups) are statistically different from
each other)
continuous variables
Take on any value within a range.
Measured on interval and ratio levels
Dummy variables
Represent dichotomous
variables
Take on the value of 0 or 1
positive correlations
Represent dichotomous
variables
Take on the value of 0 or 1
Negative correlations
As one value increase, the other decreases
univariate analysis
- describining one single variable
- aims to summarize data
- not explain relationships
bivariate analysis
- examines the relationships between two variables
- the relationships betwenn independent and dependent variables
multivariate analysis
analyzes multiple variable at the same time
- used to understand complez relationships between multiple factors
populations
the entire group you want to understand
