1. If the sample size decreases then the sample mean increases. 2. If the population distribution deviates largely from normality then larger samples will be needed to approximate normality. 3. Point estimate calculated from the estimated sample data to the unknown population parameter. 4. Large samples have narrower widths than small samples. 5. Higher confidence levels have wider intervals than lower confidence interval. 6. Normal distribution can be used for approximate binomial distribution. 7. The estimated standard error is the standard distance between the sample and population mean. 8. A sample size of 25 is sufficient to obtain a normal sampling distribution. 9. Central Limit Theorem will satisfy if there are many small random events to be tested. 10. Interval estimation provides a range of values that may contain the unknown parameter