Interpret the interval. We are 95% confident that the true average blood lead level for male workers is greater than that of female workers by an amount within the confidence interval. We are 95% confident that the true average blood lead level for male workers is less than that of female workers by an amount within the confidence interval. We cannot draw a conclusion from the given information. We are 95% confident that the true average blood lead level for male workers is greater than that of female workers by an amount outside the confidence interval.

Step-by-step explanation:

There are key notes here

- *95% confidence or 95% confidence interval*

This is a popular percentage used in finding a statistic (a statistic is a parameter or measure which has values). A 95% confidence interval for a parameter is a wide range of values within which the specific value of the statistic you're testing should lie or does lie. Remember that a range is an interval from a certain value to a higher value e.g. a range of/from (8 - 18). If the parameter you're testing is the mean (the numerical average of a group) for example and that is your range then it means that you are 95% certain (almost completely or almost 100% certain) that the population mean lies within that interval. In the case of this question, there are no numerical values for the mean (average)

- *true average*

True average means the true mean value for the population

- *an amount within the confidence interval*

In this case, we're comparing the mean/average for male workers and female workers so DIFFERENCE IN MEANS is the actual statistic we are looking at here. So, for (A)

(A) We are 95% confident that the difference in mean of blood lead level in male workers and female workers is an amount or value that lies within the confidence interval created

(B) Same as (A) above BUT here, we're testing that (our hypothesis is) the true mean blood lead level for male workers is LESS THAN that of female workers.

(C) If a conclusion cannot be drawn from the statistical information given, that means we can't find an accurate cause-and-effect relationship between the independent variable(s) and dependent variable(s)

(D) Here, our hypothesis (what we are testing or what we seek to know) is whether the true mean blood lead level for male workers is GREATER THAN that of female workers. Next, we are using a 95% confidence interval. Now, the DIFFERENCE IN MEANS which is what we are measuring falls outside the confidence interval created. This means that the actual value of the difference in means is either less than the lower boundary value in our confidence interval OR higher than the upper boundary value in our confidence interval.

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step-by-step explanation: