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Create a post about a real-world probability sampling scenario

1. Think of a real-world scenario or research example where probability sampling is used

2. Fill in the required information:

Scenario: Describe the real-world situation or research context

Sampling Method: Identify which probability sampling method is used

Justification: Explain why this sampling method is appropriate for this scenario

Optional: You may add an image or diagram that illustrates your scenario.

# data[].attributes.custom_properties.iq904jao
We will be testing different filter types and cleaning methods. Our testing is specifically focused on the throughput of the filters over time which is a continuous data output. Systematic sampling will let us take samples of the throughput value every so often to simplify our data results. 

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We want to sample by service (general type of surgery such as cardio, dental, etc) with specific prioritization as the lead time should be different between services. 

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Cluster: measuring empirical data versus queuing formulation by service to test the validity of the queuing formulas. Our project is to reduce the scheduling lead time for surgeries at UNC Hospitals so that patients can receive surgical care faster. 

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How much effort fans are willing to put into recycling/composting?

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How much effort fans are willing to put into recycling/composting?

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There is definitely a spectrum on the amount of effort that fans are willing to do the right thing for sustainability efforts.  Random sampling makes sense here to give us a good idea of people's viewpoint on the efforts towards waste diversion.  A good scope is to get the input of season ticket holders.  Depending on the results from this sample, we can change our efforts to be geared towards the average fan's efforts.

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Novo Nordisk (HFC): Chiller Usage Experiment

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Novo Nordisk (HFC): Chiller Usage Experiment

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Scenario: Determine the proportion of time that each chiller is in use during production (4 chillers in total). This will help to determine which chillers are more critical to production for our HFC phase-out plan. Justification: We chose random sampling because there are only 4 chillers in a close geographic area (inexpensive to follow this option). It would allow for the most thorough experiment and provide data for the distribution cycle.

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Random sampling (i.e. we randomly select time to check the chillers to check which are in use) 

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Probability Sampling in the College of Engineering

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Probability Sampling in the College of Engineering

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Choose a department/center in the College of Engineering randomly to audit. This should give us an unbiased estimate of the process and workload for each department and ultimately the entire College of Engineering. Each department completes the same steps for a faculty proposal submission regardless of the department's size or the amount of money requested. Choosing a department at random would ensure that we do not include our own biases for which departments are 'best' or 'worst'. Auditing multiple departments using random sampling would give us a fantastic overview of how all of the departments function.

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Hitachi Energy (efficiency)- interviewing operators for new expansion layout

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Hitachi Energy (efficiency)- interviewing operators for new expansion layout

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There are 3 shifts at Hitachi. The first has the highest productivity and the most skilled labor. The second is a mix of skilled and newer employees, and the third shift (night shift) is used for grunt work. We want our sampling to be proportional to the productivity of each shift, meaning we would take most of our sample from first shift

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SOW Random Sampling: Randomly choose documents to verify improvements

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SOW Random Sampling: Randomly choose documents to verify improvements

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The SOWs are categorized by work type/categories, to verify improvements we should make sure each category of SOW is represented evenly in the sample so stratified would be the best use case. 

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Not realistic to do random sampling to determine when to watch the processes and tasks take place. Systematic sampling allows us to randomly select a date/time to monitor the process, while only having to randomly select one day. After that. we could do every 3rd day following the previous day we monitored the processes.

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We will be observing the Herbalife changeover and sanitation process many times in order to decrease the amount of time that it takes to complete the process.

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Our sampling method would be cluster sampling as we will all as individual team members observe changeovers on our own, and then come together to look at the data together. Meaning that we will all collect our own data and can pull from the collective data to come to a consensus.

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Novo Warehouse Consumable - Random Sampling

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Novo Warehouse Consumable - Random Sampling

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Our project focuses on maximizing warehouse space by creating an ordering schedule for consumables. By clustering, we would group a long list of consumables with similar uses and randombly pick a consumable from each cluster. i.e. tubing vs chemicals  (usage rates are going vary significantly between these consumables, so separating them would ensure precision in our overall ordering schedule).

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We will group buildings based on college and then do cluster sampling to evaluate if the college has an AED device within the building. Additionally, checking if the device works and the time it takes to get to the nearest AED device by that one sampled. 

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ECI- Selecting Machines To Improve Material Handling

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ECI- Selecting Machines To Improve Material Handling

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We are working on deciding which machines to implement our material handling solutions. How we go about deciding which machines is up for debate. There are already different departments that are in charge of certain machines (lathes, mills,etc). Each machine in every department is already numbered. So we will choose one machine from every department randomly. This represents Cluster Sampling. This will ensure we implement our solution in a wide variety of machines to ensure it works and make any adjustments needed for each department. 

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We want to collect data related to production quality issues on manufacturing lines. Stratified sampling would be ideal because we are tasked with collection on multiple lines. Collecting across various shifts and production lines is necessary to cover all cases. We want to control the proportion of sampling based on the revenue produced by each line. E.g., higher revenue producing lines will be weighted higher

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Atrium Health mammography screening results turnaround time

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Atrium Health mammography screening results turnaround time

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Scenario: Atrium Health is working to reduce the time it takes for patients to receive their mammography screening results to under 24 hours. However, at the moment their primary form of communicating these results is via a patient portal. Here, we would use weighted random sampling based on age demographics to get a better picture of how patients feel about the portal. Most of the women getting mammograms are over 40, so by weighting the age samples, we will get a better idea of patients' needs based on how many patients fall within specific age groups. 

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Hitachi Energy Sustainability: lbs of waste operators interact with 

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Hitachi Energy Sustainability: lbs of waste operators interact with 

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Justification: Different shifts have differing contributions to waste contamination, clustering by shift will allow us to see which one contributes the most. We can use this information to determine where to focus our efforts. 

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Cluster sampling: divide operators by shifts (1st, 2nd, 3rd) 