In Real Life Applications Statistics Help Us Analyze Data

In Real Life Applications Statistics Helps Us Analyze Data To Extract

In real-life applications, statistics helps us analyze data to extract information about a population. In this module discussion, you will take on the role of Susan, a high school principal. She is planning on having a large movie night for the high school. She has received a lot of feedback on which movie to show and sees differences in movie preferences by gender and also by grade level. She knows if the wrong movie is shown, it could reduce event turnout by 50%. She would like to maximize the number of students who attend and would like to select a PG-rated movie based on the overall student population's movie preferences. Each student is assigned a classroom with other students in their grade. She has a spreadsheet that lists the names of each student, their classroom, and their grade. Susan knows a simple random sample would provide a good representation of the population of students at their high school, but wonders if a different method would be better. You can review the student demographics here: Module One Discussion Data PDF .

Paper For Above instruction

My name is Alex Johnson, and I have often relied on data analysis to inform personal and professional decisions, such as evaluating customer satisfaction surveys at my previous job to improve service quality or analyzing real estate market trends to decide when to buy a house. For Susan, an inappropriate sample would be a convenience sample, such as selecting only students who are present in a specific classroom or during a specific time slot, which may not accurately represent the entire student body due to inherent biases. This method can lead to overrepresentation of certain groups and underrepresentation of others, resulting in inaccurate predictions about overall preferences.

Conversely, a well-designed sample would aim for a stratified sampling method. Since students are grouped by grade and gender, this approach involves dividing the population into strata based on these categories and randomly selecting students from each stratum proportionally. Such a sampling technique ensures that all subgroups are fairly represented, leading to more reliable insights into overall student preferences. The relationship between a sample and a population is that the sample is a subset that accurately reflects the characteristics of the entire population, allowing generalizations to be made with confidence. The two sample methods described are classified as: the convenience sample is non-probabilistic and biased, while the stratified sample is probabilistic, representative, and systematic in ensuring proportional representation across groups. Proper sampling techniques are essential for drawing valid conclusions, especially when making decisions that impact large groups, such as selecting a movie that appeals broadly to maximize attendance at school events.

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