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Fairness means ensuring your analysis doesn't create or reinforce bias. This can be challenging , but if the analysis is not objective , the conclusions can be misleading and even harmful. In this reading , you’re going to explore some best practices you can use to guide your work toward a more fair analysis! Consider fairness Following are some strategies that support fair analysis: Best practice Explanation Example Consider all of the available data Part of your job as a data analyst is to determine what data is going to be useful for your analysis. Often there will be data that isn’t relevant to what you’re focusing on or doesn’t seem to align with your expectations. But you can’t just ignore it; it’s critical to consider all of the available data so that your analysis reflects the truth and not just your own expectations. A state’s Department of Transportation is interested in measuring traffic patterns on holidays. At first , they only include metrics related to traffic volumes and the fact that the days are holidays. But the data team realizes they failed to consider how weather on these holidays might also affect traffic volumes. Considering this additional data helps them gain more complete insights. Identify surrounding factors As you’ll learn throughout these courses , context is key for you and your stakeholders to understand the final conclusions of any analysis. Similar to considering all of the data , you also must understand surrounding factors that could influence the insights you’re gaining. A human resources department wants to better plan for employee vacation time in order to anticipate staffing needs. HR uses a list of national bank holidays as a key part of the data-gathering process. But they fail to consider important holidays that aren’t on the bank calendar , which introduces bias against employees who celebrate them. It also gives HR less useful results because bank holidays may not necessarily apply to their actual employee population. Include self-reported data Self-reporting is a data collection technique where participants provide information about themselves. Self-reported data can be a great way to introduce fairness in your data collection process. People bring conscious and unconscious bias to their observations about the world , including about other people. Using self-reporting methods to collect data can help avoid these observer biases. Additionally , separating self-reported data from other data you collect provides important context to your conclusions! A data analyst is working on a project for a brick-and-mortar retailer. Their goal is to learn more about their customer base. This data analyst knows they need to consider fairness when they collect data; they decide to create a survey so that customers can self-report information about themselves. By doing that , they avoid bias that might be introduced with other demographic data collection methods. For example , if they had sales associates report their observations about customers , they might introduce any unconscious bias the employees had to the data. Use oversampling effectively When collecting data about a population , it’s important to be aware of the actual makeup of that population. Sometimes , oversampling can help you represent groups in that population that otherwise wouldn’t be represented fairly. Oversampling is the process of increasing the sample size of nondominant groups in a population. This can help you better represent them and address imbalanced datasets. A fitness company is releasing new digital content for users of their equipment. They are interested in designing content that appeals to different users , knowing that different people may interact with their equipment in different ways. For example , part of their user-base is age 70 or older. In order to represent these users , they oversample them in their data. That way , decisions they make about their fitness content will be more inclusive. Think about fairness from beginning to end To ensure that your analysis and final conclusions are fair , be sure to consider fairness from the earliest stages of a project to when you act on the data insights. This means that data collection , cleaning , processing , and analysis are all performed with fairness in mind. A data team kicks off a project by including fairness measures in their data-collection process. These measures include oversampling their population and using self-reported data. However , they fail to inform stakeholders about these measures during the presentation. As a result , stakeholders leave with skewed understandings of the data. Learning from this experience , they add key information about fairness considerations to future stakeholder presentations. ,
Appearance: The brown bear is a large and powerful creature , often considered the embodiment of strength and ferocity. It has a distinctive hump of muscle on its shoulders , which sets it apart from other bear species. Despite its name , the brown bear's fur can vary significantly in color , ranging from light brown to dark brown , and even shades of blonde or reddish-brown. Some individuals may have a lighter-colored face , creating a "grizzled" appearance , while others have a more uniform coloration throughout their body. Features: Brown bears are well-adapted for survival in various environments. They have strong limbs with sharp claws , which they use for digging , climbing , and catching prey. Their jaws are equipped with formidable teeth that can crush bones and tear through flesh. Adult brown bears can stand around 6 to 10 feet tall when on their hind legs , and their weight ranges from 400 to 1 , 500 pounds , with males being significantly larger than females. Habitat: Brown bears are versatile creatures , capable of inhabiting a wide range of habitats. They can be found in dense forests , grasslands , tundra , and mountainous regions. In your DND world , they might be common inhabitants of rugged wilderness areas , guarding their territories and foraging for food. Behavior: Brown bears are generally solitary animals , except during the breeding season or when a mother is caring for her cubs. They are omnivorous , having a diverse diet that includes berries , fruits , fish , insects , small mammals , and even carrion. Their intelligence and resourcefulness enable them to adapt to different food sources based on the season and availability. Role in the World: In your DND world , brown bears could play a significant role in the ecosystem. They might be seen as the apex predators of certain regions , balancing the population of prey animals and ensuring the health of the natural environment. Druids and rangers could have a special connection with brown bears , viewing them as symbols of the wild and protectors of nature. Encountering a brown bear in the wilderness could be a thrilling and potentially dangerous experience for adventurers. These formidable creatures are generally not aggressive toward humans , but they may become defensive if they feel threatened or if their territory is encroached upon. Adventurers might have opportunities to peacefully observe brown bears from a distance or use animal handling skills to avoid conflict. However , provoking or attacking a brown bear could lead to a fierce and challenging battle , as they are capable of inflicting significant damage with their powerful claws and jaws. ,
Appearance: The wolf is a majestic and intelligent creature that roams the untamed wilderness of your DND world. Wolves have a lean and muscular build , with a fur coat that can vary in color , including shades of gray , brown , black , and white. Their fur is usually thick and provides protection against harsh weather conditions. Adult wolves typically stand around 2 to 3 feet tall at the shoulder and can weigh between 60 to 150 pounds , with males being larger than females. Features: Wolves have keen senses , including exceptional hearing and a powerful sense of smell. Their sharp teeth and strong jaws are designed for hunting and consuming a wide range of prey. They have long legs , allowing them to cover great distances quickly when pursuing prey or patrolling their territory. Wolves' paws have well-developed pads , enabling them to move silently and skillfully through various terrains. Habitat: Wolves are highly adaptable creatures and can be found in a variety of habitats , including forests , grasslands , and tundra. They often inhabit remote and untouched regions , away from human settlements. In your DND world , they might roam vast stretches of wilderness or even be associated with specific forested areas. Behavior: Wolves are social animals , living in close-knit packs led by an alpha male and female. They exhibit strong family bonds , working together to hunt and protect their territory. In your DND world , packs of wolves might be seen as symbols of loyalty , teamwork , and the balance of nature. Lone wolves could also be encountered , either as solitary hunters or as outcasts from their original packs. Role in the World: In your DND world , wolves might play an essential role in the ecosystem , helping to control the populations of herbivores and maintaining a healthy balance in the natural environment. Druids and rangers might have a special connection with wolves , viewing them as guardians of the wild and emblems of freedom. Encountering wolves in the wild could present various opportunities for adventurers. They might have chances to peacefully observe wolves from a distance or use animal handling skills to communicate with them. In certain situations , adventurers could witness the social dynamics of a wolf pack or even be assisted by friendly wolves in their quests. However , engaging in combat with a pack of wolves could be a challenging encounter , as their coordinated attacks and pack mentality make them formidable predators. ,