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There are several reasons that experiments with faulty designs or with inconsistent data are problematic for scientists. A person can make one of those problems if he or she were to poorly measure what they are studying. For example, someone measured the mass of a book correctly to be 2 pounds, and someone else measured it mistakenly to be 1 pound. Another way that a person can make problems with faulty designs and inconsistent data is the lack of accuracy and precision. This could happen when someone can have the value of 10 from a correct data set of 9, 10, 10, 11, and 12, and someone else can have the value of 10 from an incorrect data set of 5, 7, 19, 15, and 10. The first data set has a lot more precision that the second data set. Another example would be: Someone could have the value of 10 from a correct data set of 9, and 11. Someone else can have the value of 10, but have the incorrect data of 7, and 15. The first set has more accuracy than the second set. A third reason that faulty designed experiments and inconsistent data can happen is the flawed experiments. For flawed experiments to happen, they may be uncontrolled, untrustworthy conclusions, or being inconsistent with other tests performed. For the last reason that they can happen, there can be bias. this could happen when the samples are too small, not randomly selected samples, and some outliers are present.