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s as compared to false positives (Type I errors) since doctors rarely get a second chance to assess people who believe that they are healthy and may thus not return. In order to reduce the chance of errors leading to false negatives, the government could set in place a system where each sample is tested twice, thus reducing the chance that a consistent false negative will be obtained. If this is not possible, testing procedures could be tried with larger samples of people or the criteria for recording a negative be made more stringent (accept more ambiguous positives).

Thus, P(P1 and H1) ≠ P(P1) · P(H1). Similarly, using the equation shows that P(P1 and H2) ≠ P(P1) · P(H2); P(P2 and H1) ≠ P(P2) · P(H1) and P(P2 and H2) ≠ P(P2) · P(H2).This means that the two conditions are not independent of each other, but have a strong association. The distribution presented is positively skewed, as is evident from the difference seen between the calculated mean, median and mode. Given this, the median would be the most appropriate measure of central tendency to use with this distribution since it is robust against the change in extreme values.

The mean would be biased towards the higher scores whereas the mode would not be appropriate as it would be biased towards the lower end of the distribution. The data in this distribution is not normally distributed, but has a positive skew. This is evident from the measures of central tendency, the frequency distribution and the histogram. The distribution has a high mean and relatively lower median with the value for mode being lowest. The frequency distribution shows that over half the frequencies are found in the first two groups and the frequencies taper off towards the higher side.

The histogram also verifies that the frequencies are bunched to the lower end of the distribution. Thus, this is positively skewed and not a normal