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For each data set, which aspect of the data is likely to cause evidence obtained from it to be inconsistent?

Data Set I: [tex]190, 315, 198, 194, 200, 195, 540, 201, 197, 193[/tex] ("correct" value [tex]=195[/tex])

A. Lack of accuracy
B. Lack of precision
C. Outliers

Data Set II: [tex]18, 51, 52, 50, 50, 49, 51, 53[/tex] ("correct" value [tex]=65[/tex])

A. Lack of accuracy
B. Lack of precision
C. Outliers

Data Set III: [tex]9, 13, 14, 19, 11, 20, 19, 12[/tex] ("correct" value [tex]=15[/tex])

A. Lack of accuracy
B. Lack of precision
C. Outliers

Sagot :

GinnyAnswer
To determine the aspect of the data causing inconsistencies in each data set, we should analyze how the values deviate from the "correct" value:

Data Set I: [tex]\(190, 315, 198, 194, 200, 195, 540, 201, 197, 193\)[/tex] with a correct value of [tex]\(195\)[/tex].
- In this data set, most of the values are clustered around [tex]\(195\)[/tex], but there is one value ([tex]\(540\)[/tex]) which is significantly larger than the others. This extreme value is known as an outlier and can greatly impact the overall statistics of the data set.
- Therefore, the inconsistency here is caused by Outliers.

Data Set II: [tex]\(18, 51, 52, 50, 50, 49, 51, 53\)[/tex] with a correct value of [tex]\(65\)[/tex].
- The values in this data set are all relatively close to each other, but they are all significantly lower than the correct value of [tex]\(65\)[/tex]. This indicates that the measurements are consistently off the mark.
- Therefore, the inconsistency here is due to a Lack of accuracy.

Data Set III: [tex]\(9, 13, 14, 19, 11, 20, 19, 12\)[/tex] with a correct value of [tex]\(15\)[/tex].
- The values in this data set are spread out around the correct value of [tex]\(15\)[/tex], with some being significantly lower and some being significantly higher. This spread suggests that measurements vary widely.
- Therefore, the inconsistency here is due to a Lack of precision.

Thus, the evidence of inconsistencies in the data sets can be summarized as follows:

- Data Set I: Outliers
- Data Set II: Lack of accuracy
- Data Set III: Lack of precision

In conclusion:
- Data Set I is flagging Outliers as the cause of inconsistency.
- Data Set II is flagging a Lack of accuracy as the cause of inconsistency.
- Data Set III is flagging a Lack of precision as the cause of inconsistency.
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