Inferential statistics are a branch of statistics that allows us to
draw conclusions, make predictions, or infer characteristics about a ‘population’ based on data collected from a ‘sample’
Inferential statistics draw generalizations and inferences from
‘sample’ statistics to the ‘population parameters’
Inferences are probabilistic because
even with adequate sampling procedures such as random sampling, always a chance of sampling error
Confidence intervals
sample statistics are estimates of actual population parameters
Confidence intervals: sampling errors = ???
difference between sample statistics and true population parameters
We use inferential statistics to estimate
probably sampling error
Central limit theorem
the sampling distribution of the sample mean will be approximately normal if the sample size is large enough, regardless of the population’s original distribution
For confidence intervals the researcher decides
probability level
Common confidence intervals
p = 0.95 (95% confidence interval)
p = 0.99 (99% confidence interval)