Confidence intervals
A point estimate gives one parameter value, but sampling variation means that estimate is uncertain. A confidence interval describes uncertainty using a repeated-sampling procedure.
Interpretation
A 95% confidence procedure is constructed so that, under its assumptions, 95% of intervals produced over repeated comparable samples contain the true parameter value.
It does not mean that a fixed parameter has a 95% probability of lying inside one already calculated frequentist interval.
Methods
Intervals can be obtained using standard errors and asymptotic approximations, profile likelihood, or bootstrap procedures. Their reliability depends on model assumptions and data informativeness.
Key idea. Confidence intervals quantify sampling uncertainty in an estimator, not certainty that the fitted model itself is correct.