# data[].attributes.body
Create a post about a real-world nonprobability 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 nonprobability sampling method is used

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

Include one type of potential Sampling Bias, Selection Bias and Error that could be introduced in your analysis.

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

# data[].attributes.custom_properties.iq904jao
Sampling bias: As our group is going on a certain day, the operators will likely be informed of our presence and may work to their best ability. We will be paired with engineers and will observe the process with them - meaning that engineers will be observing the operators, causing them to work at a higher standard. 

# data[].attributes.custom_properties.iq904jao
We are sampling people to see how much effort they will put in to recycling/composting.  We will each stand by a gate and sample the first 20 fans that walk through who are willing to talk to us. This will be convenient for us because if we were to send out an email we would get biased or minimal responses.  However, there is still self-selection bias here as people who are passionate about recycling will likely be thrilled to speak to us, while others who don't care will likely ignore us or show their dissatisfaction.  Sampling bias would be the range of people based on each gate we are standing at.  To combat this, we would send David to the student gate, Michael to the Townebank center, Billy to the north end zone, and John to the east gate.  Errors include people lying to us about their willingness to help.

# data[].attributes.custom_properties.iq904jao
Scenario: We want to determine the most common/urgent concerns the facilities/maintenance teams at NN and Brady (third-party refrigerant services) have about different refrigerant alternatives.                                               Justification: We want to sample subject matter experts to gain the most information about potential issues with using alternative refrigerants.           Sampling Bias: Survivorship bias is introduced because technicians want to work with refrigerants that they have good experiences with.            Selection Bias: There could be people outside of those teams with valuable feedback (e.g. Katie Roberts).                                                                 Error: People could randomly misremember their experiences and give false information. 

# data[].attributes.custom_properties.p1tZfmWo
Purposive: The people who work the most with refrigerant will be sampled for their expertise.

# data[].attributes.custom_properties.iq904jao
For our project at Hitachi, there are three shifts which all have operators of differing expertise. We will be sampling the operators who work in the 6:30-3 shift as this time works best for us to be on site. This sampling is appropriate because the most skilled operators work during this shift and we feel they will be the best to sample. Sampling bias is telling the operators that if anyone wants to speak to us, we are available. Participants would then opt-in to sharing ideas with us. Selection bias is only talking to first shift instead of talking to all three shifts. 

# data[].attributes.custom_properties.iq904jao
We decided to use convenience sampling for our data collection because our 'audits' can only be completed on departments with the time and resources to meet with us. This makes it very hard to perform random sampling or the other methodologies. Example of Sampling Bias - Since we are only selecting departments willing to meet us, we may get skewed data if we try to apply their answers to the whole college. This is because the departments willing to meet are probably those operating with a better model already. Example of Selection Bias - Since we are dividing and conquering our 'audits', we will pick up on and interpret things differently between the departments. Although we have a template to ask the same questions, this could lead to inconsistencies while collecting our data. Example of Error - department heads or staff do not provide accurate information whether because they do not want to share their inadequacies or they do not know the correct answers.

# data[].attributes.custom_properties.iq904jao
In order to speak directly with patients and get the most unbiased participation, we have to do this in person. Since the locations are in Winston-Salem, we are not able to visit everyday to get these results. 

# data[].attributes.custom_properties.p1tZfmWo
Scenario: Attempting to discern the percentage of patients who have and use an Atrium Health MyChart account. Sampling Method: Convenience Sampling - only sampling from patients who have an appointment on the day we are visiting.  Sampling Bias: By only speaking to patients on one day, we may introduce bias of certain times of day and days of the week. 

# data[].attributes.custom_properties.iq904jao
The other methods are not really applicable to our project. Our schedule only allows for us to go at the same time of day/ same day, so we will likely be seeing the same shifts of operators.

# data[].attributes.custom_properties.p1tZfmWo
Convenience Sampling: we will just pick a team lead that is currently at the plant while we are there. Sampling Bias: under coverage; we will only be covering one shift, and there are only managers around during the day shift, so the production is much higher during day shift.                                      Selection Bias: observation bias; people working will perform higher when we are there                          Error: we may not see all of the issues they have while we are there, because we will not be there long enough   

# data[].attributes.custom_properties.iq904jao
Scenario: attempting to reduce scheduled lead time for surgeries in the OR at UNC. This project was told to ignore all qualitative factors, so this is a hypothetical.  Justification: Prioritization of type of surgery needed (cardiac (high) vs. dental (low)) 

# data[].attributes.custom_properties.p1tZfmWo
Quota Sampling , Sampling bias: data is only from one year, selection bias: undercoverage - if demand from this year is not consistent with the year before, then a certain type of surgery will be underrepresented. Error: re-scheduled surgeries are not provided in our data, which causes lead time to be inaccurate.

# data[].attributes.custom_properties.iq904jao
We are only going to the site on days that are convenient for our team. We will only be speaking to people during first shift, and the operators or managers that are there. The error associated with our project is the data we are given. To create our mathematical model we are gathering data from the team, and we are assuming it is correct. It is possible that this data isn’t correct or is outdated. This incorrect data can then lead to error for our model. Undercoverage bias is possible for our project because we are only talking to first shift employees. We are not speaking to everyone on site, so we aren’t gathering and hearing everyone’s opinions. 

# data[].attributes.custom_properties.iq904jao
Our project involves testing different chemical solutions for a filter cleaning cycle. We have a wide variety of possible chemicals to test but we will be sampling specific ones to narrow our scope. This will be a convenience sample since Believer Meats will be providing chemicals they have already deemed as feasible. For sampling bias, Believer Meats has already pre selected samples for us to test, limiting our options potentially causing us to miss chemicals which would also be suitable. For selection bias, we may have to split up into groups for site visits, which could potentially cause bias if different experimenters interpret or observe results differently. Random errors can occur if KPIs are misread and recorder incorrectly. Systematic errors can occur if equipment is not calibrated correctly which will skew all of our results.

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Hitachi Energy Sustainability: Waste weight by shift 

# data[].attributes.subject
Hitachi Energy Sustainability: Waste weight by shift 

# data[].attributes.custom_properties.iq904jao
Our team has limited time on the production floor due to the fact that the plant is 1+ hours away. Because of this, we are only using samples from when we are available to go... this does not include 3rd shift operators                                                                              Bias introduced:                                                  >Selection due to our Recruitment process (focusing on 1st and 2nd shifts)                    >Sampling error due to the under coverage of certain shifts (3rd shift is skeleton crew)                                        Error introduced:                                                                Systematic error - Operators will seem to perform better is they are being observed. This will positively skew our results.                                                          

# data[].attributes.custom_properties.iq904jao
In order to decide what days to monitor the processes at our site, we have used systematic sampling. We will go in on certain days, determined by group member availability each week, more than likely on specific days of the week (Thursday, Friday). This sampling method is appropriate due to time/scheduling constraints that we have as a team. We could run into sampling bias in that we have chosen what days to observe the process. There may be products produced that day or processes in operation that are different on other days of the week and we will miss those differences. This will limit the generalization of our conclusions for those processes. In terms of selection bias, we may more specifically observe a type of selection bias called survivorship bias. While we monitor the processes occurring at the facility, the operators may work harder/faster than they normally would. To address this, we could instead set up covertly placed recording devices to monitor the processes/operators. Error wise, we could run into random error when conducting any time or motion studies. It's certainly possible to occasionally incorrectly categorize certain movements during a motion study, or making a simple mistake when filling out a time study.

# data[].attributes.custom_properties.iq904jao
Scenario: Convenience sampling of AEDS across main campus, it is easier to focus on one section of NC state in the timeline given. AEDs checked on main campus will be checked if they properly cover the student body. We would check if the device works. Justification: non-probability method allows us to convientally sample AED devices. Sampling Bias: Does not include centennial campus building or agriculture in the cluster sampling. Selection Bias: We don't take into account staff who would also be potentially using AED devices.

# data[].attributes.custom_properties.iq904jao
Project: creating an ordering system for consumables in Novo Nordisk Warehouse. Sampling Method: Puposive Sampling - we are collecting data from multiple, relevant department representatives (i.e. procurement for ordering processes, batch team supervisors/operators for real-time consumption rates, etc.). Sampling bias - Selection bias - when collecting the data that we currently have, there are more operators on the line than during a normal batch. Error - first batch data (which is what we currently have) may not accurately represent average batch production data. 

# data[].attributes.custom_properties.iq904jao
We are performing customer discovery for our Lockout/Tagout product in the entrepreneurship program. We have performed 2 rounds of discovery so far, and we have one more round coming up. LOTO systems are business-to-business, so we have been reaching out to employees of businesses in the manufacturing/construction industries.
When we are collecting the samples, we may interpret them a certain way because of our own experience in the manufacturing/construction industries.
We reached out to our industry contacts because we found cold-calling to be ineffective. We did not tell participants about our product- but the groups of people mostly consisted of management which could have affected results.
Our results could be inaccurate if the participants felt pressured to answer a certain way, if we did not study the correct group of employees, or if the data is skewed to only represent one industry. 

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FRC - Selecting documents to measure consistency

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FRC - Selecting documents to measure consistency

# data[].attributes.custom_properties.iq904jao
We would use purposive sampling because it would allow us to choose documents that are most useful to testing consistency, more specifically we could choose documents that SHOULD share a common set of characteristics and validate the consistency between them. For example, SOW documents for procuring 5 axis CNC machines. Making sure that all the documents have the appropriate level of consistency/standardization between them.