# Data Analysis Workout 11 - Data Literacy: Navigating the Data-Driven Landscape

Data literacy is the ability to read, understand, and communicate with data. In this workout, immerse yourself in the basics of data interpretation, questioning, and critical thinking in the realm of data.

Scenario:

You’re presented with various data scenarios, statistics, and visualizations. Your role is to interpret them correctly, spot inconsistencies, and ask the right questions.

Objectives:

By the end of this workout, you should be able to:

1. Interpret data-driven statements and visualizations.

2. Recognize potential pitfalls or misrepresentations in data.

Given the following scenarios, answer the related questions:

1. A news article claims: “90% of people prefer Brand A over Brand B.”

• What additional information would you want to validate this claim?

2. You’re presented with a pie chart showing the distribution of market shares among five companies. Company A appears to dominate the market.

• What potential biases or data collection methods might skew this representation?

3. A study states: “People who drink Green Tea are 30% less likely to catch a cold.”

• What potential confounding variables can you think of that might influence this result?

Questions:

1. Why is it essential to consider the sample size when interpreting a data-driven claim?

• i) Larger sample sizes always produce accurate results.

• ii) Smaller sample sizes can lead to more significant variability and potential bias.

• iii) Sample size affects the color of data visualizations.

• iv) Only large sample sizes are valid in research.

2. If a data visualization doesn’t have a labeled axis or provides unclear units, what should be your immediate reaction?

• i) Assume average values for the data.

• ii) Consider the visualization to be entirely accurate.

• iii) Question the credibility and clarity of the visualization.

• iv) Redraw the visualization yourself.

Duration: 25 minutes

Difficulty: Beginner

Period
This workout will be released on Tuesday, September 5, 2023, and will end on Thursday, September 28, 2023. But you can always come back to any of the workouts and solve them.

[quote=“EnterpriseDNA, post:1, topic:46464”]
Given the following scenarios, answer the related questions:

1. A news article claims: “90% of people prefer Brand A over Brand B.”
• What additional information would you want to validate this claim?

1. You’re presented with a pie chart showing the distribution of market shares among five companies. Company A appears to dominate the market.
• What potential biases or data collection methods might skew this representation?

• Your Answer: I’m not sure what market shares are. However, biases could include sample size, how many shares we’re talking about, where the information came from.

1. A study states: “People who drink Green Tea are 30% less likely to catch a cold.”
• What potential confounding variables can you think of that might influence this result?

• Your Answer: There might be other reasons why the green tea drinkers are less likely to catch a cold. Maybe Green Tea drinkers don’t interact so much with other people. Also, how is it determined that someone has a cold?

Questions:

1. Why is it essential to consider the sample size when interpreting a data-driven claim?

ii) Smaller sample sizes can lead to more significant variability and potential bias.

2. If a data visualization doesn’t have a labeled axis or provides unclear units, what should be your immediate reaction?

iii) Question the credibility and clarity of the visualization.

Question the credibility and clarity of the visualization.

Thanks
Ankit J

Here is my solution to this workout:

Questions:

1. Why is it essential to consider the sample size when interpreting a data-driven claim?
• ii) Smaller sample sizes can lead to more significant variability and potential bias.
1. . If a data visualization doesn’t have a labeled axis or provides unclear units, what should be your immediate reaction?
• iii) Question the credibility and clarity of the visualization.

1. A news article claims: “90% of people prefer Brand A over Brand B.”
• What additional information would you want to validate this claim?

To validate the claim “90% of people prefer Brand A over Brand B,” you would want additional information such as:

1. Sample Size: How many people were surveyed? A larger sample size can provide a more accurate representation of the population.

2. Demographics: What are the demographics of the people surveyed? Age, gender, location, and other factors can influence brand preference.

3. Methodology: How was the survey conducted? Was it an online poll, a phone survey, or conducted in-person? The methodology can impact the results.

4. Question phrasing: How was the question phrased in the survey? The wording can significantly influence how people respond.

5. Timeframe: When was the survey conducted? Preferences can change over time, so recent data is generally more relevant.

6. Conflict of Interest: Who sponsored the survey? If the survey was sponsored by Brand A or B, there might be a bias in how the survey was conducted or reported.

1. You’re presented with a pie chart showing the distribution of market shares among five companies. Company A appears to dominate the market.
• What potential biases or data collection methods might skew this representation?

Several potential biases or data collection methods might skew the representation of market shares among the five companies:

1. Sampling Bias: If the data was collected from a non-representative sample, it might not accurately reflect the true market share. For example, if the survey was conducted only in a region where Company A is popular, it would show a skewed preference for Company A.

2. Timeframe: The data might be outdated or collected during a period that favors Company A. For example, if Company A had a major sale or promotion during the data collection period, it could temporarily boost their market share.

3. Data Collection Method: The method of data collection can also introduce bias. For example, if the data was collected through customer surveys, it could be biased towards customers who are more likely to respond to surveys (which might be customers of Company A).

4. Market Definition: How is the market defined? If it’s too broad or too narrow, it might not accurately reflect the competition among the companies.

5. Conflict of Interest: If the data was collected or reported by an entity with a vested interest in Company A, there might be a bias in how the data was collected, analyzed, or presented.

6. Misinterpretation of Data: Pie charts represent part-to-whole relationships. If the total market share doesn’t add up to 100%, or if there are other companies not represented in the chart, it could give a misleading picture of Company A’s dominance.

1. A study states: “People who drink Green Tea are 30% less likely to catch a cold.”
• What potential confounding variables can you think of that might influence this result?

Several potential confounding variables might influence the result of the study stating that “People who drink Green Tea are 30% less likely to catch a cold.” These could include:

1. Overall Health: People who drink green tea might generally lead healthier lifestyles, which could contribute to their reduced likelihood of catching a cold.

2. Diet: The diet of the individuals in the study could play a significant role. People who drink green tea might have healthier diets overall, which could boost their immune systems.

3. Exercise: Regular physical activity can boost the immune system and reduce the likelihood of catching a cold. If green tea drinkers are more likely to exercise, this could be a confounding variable.

4. Age: Age can influence immune function. If the green tea drinkers in the study are younger on average, they might be less likely to catch a cold regardless of their tea consumption.

5. Geographic Location/Climate: The likelihood of catching a cold can vary depending on geographic location and climate. If green tea drinkers are concentrated in a particular area with fewer cold viruses, this could skew the results.

6. Socioeconomic Status: Socioeconomic status can influence health outcomes, including susceptibility to colds. If people who can afford to regularly drink green tea have higher socioeconomic status, they might have better access to healthcare and overall health.

7. Stress Levels: Chronic stress can weaken the immune system and make individuals more susceptible to infections like colds. If people who drink green tea have lower stress levels, this could be a confounding factor.

Thanks for the workout.
Keith

Regarding the pie chart showing the dominance of Company A in the market, potential biases or data enriching methods that might skew this representation include:

``````1. If the data only includes customers from certain demographics or geographical regions, it may not accurately represent the entire market.
2. Companies might report their market share differently, possibly inflating their dominance.
3. If the data was collected through self-reporting or from biased sources, it could lead to skewed results.
``````