Having statistical knowledge at work can have advantages, even if you aren\'t a

Question

Having statistical knowledge at work can have advantages, even if you aren't a statistical analyst yourself. Without this knowledge, interpreting and understanding many business studies can be difficult or worse, dangerous, because you can't evaluate the results yourself, and must trust those presenting results to you. When you are able to share this knowledge with colleagues, it can help them and your company make better decisions. Suppose you and several coworkers recently attended a presentation where some statistical results of an important study were shown. How would you respond to the situations below a) Halfway through the presentation there is a break. During the break, your boss comes over and mentions she noticed they were using t-tests throughout the presentation, but she isn't sure that was the right choice. Using your own words, explain the differenceeen a z-test and a t test. A junior-level teammate noticed your comments to your boss and shortly after the presentation he stops by your desk to ask a couple questions. In your own words, explain to him what hypothesis testing is, how it can be useful, and the six general steps of running a hypothesis test. That same associate also asks if you could explain what 'statistical significance' is and how you determ Later that evening you are reflecting on your day, and you remain concerned that some of the data presented relied on very low sample sizes, and you believe the presenters showed results that were less reliable than they claimed. Who would you talk about this at work, and what would you say? b) c) ine if a difference is big enough to be significant d)

Explanation / Answer

A.

A z-test is used for testing the mean of a population versus a standard, or comparing the means of two populations, with large (n ? 30) samples whether you know the population standard deviation or not. It is also used for testing the proportion of some characteristic versus a standard proportion or comparing the proportions of two populations.
Example: Comparing the average engineering salaries of men versus women.
Example: Comparing the fraction defectives from 2 production lines.

A t-test is used for testing the mean of one population against a standard or comparing the means of two populations if you do not know the populations’ standard deviation and when you have a limited sample (n < 30). If you know the populations’ standard deviation, you may use a z-test.
Example: Measuring the average diameter of shafts from a certain machine when you have a small sample.

B. The best way to determine whether a statistical hypothesis is true would be to examine the entire population. Since that is often impractical, researchers typically examine a random sample from the population. If sample data are not consistent with the statistical hypothesis, the hypothesis is rejected.

Usefulness of hypothesis:

1. It would be inefficient to do research without a hypothesis

2. The evidence gathered would be weak because you will generate hypotheses after the fact.

Steps of Hypothesis:-

C.

Statistical significance is a measure of whether your research findings are meaningful. More specifically, it’s whether your stat closely matches what value you would expect to find in an entire population. In order to test for statistical significance, perform these steps:

D. I would talk to the one who has collected the data and tell him to get reliable data.

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