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# Brief Explanations: For significance tests, conditions often involve random sampling, normality (either population is normal or sample size is large for Central - Limit - Theorem application), and independence. For confidence intervals, similar conditions like randomness, appropriate sample size for normality approximation, and independence are checked. If conditions are met for significance tests, results are more reliable in making decisions about null hypothesis. For confidence intervals, meeting conditions ensures the interval is likely to contain the true population parameter. Concerns arise when conditions aren't met, like non - random sampling leading to bias, or small non - normal samples for which normal - based methods may not be valid. # Answer: For significance tests, check random sampling, normality/adequate sample size for normality approximation, and independence. For confidence intervals, also check randomness, sample - size related normality, and independence. If conditions are met, results are more reliable; if not, there are concerns about bias and validity of methods.