Final answer:
The untrue statement about sample size is that it ensures complete information. A larger sample size allows assumptions of normality but does not guarantee completeness or stability of the information or the solution.
Step-by-step explanation:
The statement that is NOT true about sample size is "Having a large sample size helps to ensure that we have complete information requirement." While a large sample size can provide a more reliable estimate of a population parameter and can, due to the Central Limit Theorem, allow for the assumption that our response and coefficients will have a normal distribution assuming the sample size is large enough (typically n ≥ 30), it does not guarantee that the information is complete or that the solution is stable. A large sample size reduces sampling variability and makes for a better, more reliable statistic, but it cannot ensure completeness because the quality of data and sampling technique is also essential. Furthermore, if the sampling method is biased, even a large sample size would not make the sample representative of the population. Therefore, option (b) "Having a large sample size helps to ensure that we have complete information requirement." is the incorrect statement about sample size.