Data limitations, such as missing values, can significantly affect the validity and reliability of statistical estimations and tests of differences. Missing data can reduce the accuracy of a sample, lead to bias in the results, and invalidate estimations. Furthermore, it can be difficult to determine how much missing data affects the results since its presence is often not known until after analysis has been conducted.

An example of how missing values can affect statistical estimates is when one or two observations are removed from an otherwise balanced dataset resulting in an imbalance which may skew the results. For instance, a survey with a large number of responses might become unbalanced if a small number randomly selected individuals do not respond. As such, their answers would be excluded from the analysis but could still have an effect on the overall outcome if they had responded differently than those who did complete it.

Missing values also impact tests for differences between groups or samples due to their unequal representation across categories. This imbalance may result in inaccurate conclusions being drawn since less valid comparisons are made with more incomplete information than complete information (i.e., all cases within each group accounted for). If there is significant variation within groups that goes undetected due to lack of data points then this could impact any conclusion about difference between them as well as other correlations that exist across both sets of data points included in comparison statistics like t-tests or ANOVAs (Analysis Of Variance).

Additionally, relying on imputation methods for filling out gaps where no value was recorded initially will further complicate matters by introducing additional errors into your datasets unless done very carefully and cautiously; particularly when dealing with complex datasets requiring sophisticated techniques like multiple imputation approaches that use multiple values rather than just replacing any single omitted value with something arbitrary like 0 or mean/median/mode averages etc.. Otherwise these techniques produce biased estimates which are highly influenced by how many variables were used in initial imputation process and what assumptions were made about distributional properties inherent underlying population represented by dataset itself – something outside researcher’s control yet has direct bearing upon validity & reliability expected outcomes!

Overall these issues suggest that caution should be taken when encountering missing values during analyses so as not to draw erroneous conclusions from potentially flawed estimations and tests of differences due to inadequate representations caused by incomplete datasets’ distributions across categories equalized only superficially on paper yet not necessarily reflecting entire reality phenomenon under investigation based upon original parameters themselves!