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To understand why the statement indicating that Group A is positively skewed while Group B is approximately normal is accurate, we need to break down the concepts of skewness and what it implies about the distribution of data.

A positively skewed distribution, also known as right-skewed, means that the tail on the right side of the distribution is longer or fatter than the left side. This indicates that there are a substantial number of high values in the dataset, which pull the mean of the distribution to the right, away from the median. In practical terms, if the data from Group A displays this behavior, we would expect to see a concentration of lower values with a few higher outliers, resulting in the rightward tail.

On the other hand, an approximately normal distribution reflects a symmetric distribution where values are evenly spread around the mean, which is also where the median lies. For Group B to be described as approximately normal, it suggests that the data points are forming a bell-shaped curve, without significant skew in either direction.

The information given about skewness directly refers to the distribution shape and how data values are positioned relative to the mean and median. Thus, stating that Group A is positively skewed and Group B is approximately normal aligns with