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).
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!
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