1. Alice and Bob created problems for HackerRank and were rated on three categories: problem clarity, originality, and difficulty.
2. The task is to compare their ratings in each category and award points accordingly.
3. The function compareTriplets takes in the two sets of ratings and returns the total points earned by each person.
The article titled "Compare the Triplets" on HackerRank describes a task where two challenges created by Alice and Bob are rated by a reviewer based on problem clarity, originality, and difficulty. The ratings are represented as triplets, and the task is to compare them to determine the comparison points earned by each person.
The article provides clear instructions on how to approach the task and gives an example of how it should be done. However, there are some potential biases in the article that need to be considered.
Firstly, the article assumes that both Alice and Bob have created challenges of equal quality. This assumption may not always hold true in real-life situations, where one person may have more experience or expertise than the other. Therefore, it is important to consider this bias when interpreting the results of the comparison.
Secondly, the article does not provide any evidence or justification for why problem clarity, originality, and difficulty are chosen as criteria for rating challenges. Other factors such as relevance, usefulness, and creativity could also be considered when evaluating challenges. Therefore, it is important to recognize that this choice of criteria may not be comprehensive or objective.
Thirdly, the article only considers a binary outcome for each comparison point (i.e., either Alice or Bob earns a point). This approach does not account for cases where both challenges are equally good or bad in a particular aspect. Therefore, it is important to acknowledge that this approach may oversimplify the evaluation process.
Overall, while the article provides clear instructions on how to complete the task of comparing triplets, it is important to consider potential biases in its assumptions and criteria selection. Additionally, it would be beneficial if future versions of this task could incorporate more comprehensive evaluation criteria and account for cases where both challenges are equally good or bad in certain aspects.