You’re currently assigned to the mobile products team at intel, supporting their marketing efforts. you’ve been watching sales data come in for a specific product line, the particles chips. intel invented these chips in 2010 and has held the market ever since. microprocessors have become increasingly revolutionized for the rapid rise of smart phones. a new competitor has entered the market to compete against particles, and you’ve noticed that the customer defection rate had a sharp increase last month. the percentage of repeat customers buying particles from us declined suddenly. you know that the cost of acquiring a new customer is higher and less efficient than continuing to sell to an existing customer. what would you do?

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.