1. Describe a time when your personal or professional data is used in a decision. This could be anything from analyzing sales data on the job to making an informed purchasing decision about a home or car.
Personal and professional data are very crucial for decision-making purposes. Even though a majority of people rely on their intuition or gut feeling when making decisions, research shows that data-driven decision-making (DDDM) is taking shape in most parts of the world (Stobierski, 2019). The most common personal data that is used in making decisions is the geo-location data. Technological gadgets such as smartphones and laptops use applications and software that can pinpoint one’s geo-location according to one’s GPS and IP addresses (Nanos, 2018). This information is then sold to digital marketers, who then send tailored ads to one’s device (Nanos, 2018). Unlike the traditional forms of advertisements, the GPS and IP address-based ads allow the marketers to know exactly what one needs, depending on their internet searches. For example, in September, I wanted to relocate and so I Google-searched apartments. To my amusement, I started receiving housing ads on Facebook, YouTube, among other platforms.
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2. Describe an example of a sample of this student population that would not represent the population well.
A sample that does not represent the population well is biased to an extent that it does not reflect what the population looks like. In the case study, if Susan does not choose a sample that contains students of both genders and different grade levels, it will not reflect the entire population. Suppose the principal wants a sample of 30 students, an example of a non-representative sample would be one where the principal randomly picks the 30 students from her list without considering their genders, and grade levels. Such a scenario would lead to bias since it is not guaranteed that the two genders will be equally represented or whether each grade level will get equal representation.
3. Describe another example of a sample that would represent the population well.
A sample that represents the population well is that which accurately reflects the characteristics of the entire population. In the case study, the key characteristics are gender, grade level, and classroom. Therefore, an appropriate representative sample should have students of both genders, and from all the different grade levels and classrooms. To get a representative sample, Susan should group the students into two strata based on their gender and grade level. An example of this sample is a sample of 32 students containing a random selection of 4 girls and 4 boys in each of the 4 grade levels.
4. Finally, describe the relationship of a sample to a population and classify your two samples as random, cluster, stratified, or convenience.
A population refers to the entities from which statistical inferences are to be made (Banerjee & Chaudhury, 2010). A sample is a smaller set of entities drawn from a population to make inferences about the whole population (Banerjee & Chaudhury, 2010). In statistics, samples are often used where the number of entities in a population is large to an extent that it would be unmanageable to study the whole population (Banerjee & Chaudhury, 2010).
A sample containing a random selection of 30 students can be classified as a random sample. For instance, the random sample might contain 20 girls and 10 boys or 10 ninth graders, 6 tenth graders, 2 eleventh graders, and 12 twelfth graders.
A sample containing a random selection of 4 boys and 4 girls drawn from each grade level is a stratified sample. The stratification of students according to their gender and grade levels allows the principal to pick the same number of male and female students from each grade level.
References
Banerjee, A., & Chaudhury, S. (2010). Statistics without tears: Populations and samples. Industrial Psychiatry Journal, 19 (1), 60–65. https://doi.org/10.4103/0972-6748.77642
Nanos, J. (2018, July 5). Every step you take. The Boston Globe. https://apps.bostonglobe.com/business/graphics/2018/07/foot-traffic/
Stobierski, T. (2019, August 26). The advantages of data-driven decision-making. Harvard Business School. https://online.hbs.edu/blog/post/data-driven-decision-making
