To test the following statement: The most recent average starting salary for a graduate of a four-year college with a degree in business administration is $41,055. Your null hypothesis (H0) is set up as (denote μ to be the population mean of starting salary for a graduate of a four-year college with a degree in business administration):

Assume that the governance committee states that all projects costing more than $70,000 must be reviewed and approved by the chief information officer and the it senior leadership team (slt). at this point, the cio has the responsibility to ensure that management processes observe the governance rules. for example, the project team might present the proposed project in an slt meeting for a vote of approval. what does this scenario illustrate about organizational structure?

Jennifer purchased a house in a brand new development in the outskirts of town. when her house was built, the nearest fire department was nearly 20 miles away. as her neighborhood developed, the density of the community called for a new fire department 1.5 miles away. what effect will the new fire station have on her homeowners insurance premium? a. a new fire department will be more demanding on local taxes. her annual premium will go up. b. the location of a fire department has no bearing on the value of her house. her annual premium will stay the same. c. the new fire department will reduce the risk of financial loss in her home. her annual premium should decrease. d. with a fire department so close (less than 5 miles), financial risk on jennifer’s home practically disappears. she will not need to pay insurance anymore.

Reliability and validity reliability and validity are two important considerations that must be made with any type of data collection. reliability refers to the ability to consistently produce a given result. in the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. to illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. if the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. while the scale is highly reliable in producing consistent results (e. g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. this is where validity comes into play. validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure. while any valid measure is by necessity reliable, the reverse is not necessarily true. researchers strive to use instruments that are both highly reliable and valid.