Analysis of 3 ways to spot a bad statistic by mona chalabi
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This document summarizes Mona Chalabi's analysis of how to spot bad statistics. It outlines 3 key questions to ask: 1) Can we see uncertainty in the numbers? Visualizing data can overstate certainty. 2) Can we see ourselves in the data? Context is important to understand where data points fit. 3) How was the data collected? It's important to understand the methodology to properly interpret results. Bad statistics can mislead; we shouldn't dismiss numbers but should learn to scrutinize how they were produced and what uncertainties exist. Proper statistical analysis is important for effective policymaking and decision making.
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3 ways to spot a Bad Statistic
There are 3 questions to ask when evaluating statistics: 1) Can you see the uncertainty in the data? Many visualizations overstate certainty. 2) Can you see yourself represented in the data? Visualizations should make sense to many people. 3) How was the data collected? Government statistics are often more reliable than private statistics because of how the data is collected. Being able to evaluate statistics helps produce better decision making.
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3 ways to spot a Bad Statistic
There are 3 questions to ask when evaluating statistics: 1) Can you see the uncertainty in the data? Many visualizations overstate certainty. 2) Can you see yourself represented in the data? Visualizations should make sense to many people. 3) How was the data collected? Government statistics are often more reliable than private statistics because of how the data is collected. Being able to evaluate statistics helps produce better decision making.
ways to spot bad stats
This document summarizes a TED talk about spotting bad statistics. It outlines three questions to ask: 1) Can you see uncertainty in the data? Visualizations often overstate certainty. 2) Can I see myself in the data? National statistics may not match personal experiences. 3) How was the data collected? The method of data collection impacts the results. Government data is generally more reliable than private companies' data. Managers should maintain teams that consider data uncertainty and ensure surveys target all affected people.
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3 ways to spot a bad statistic.
Mona Chalabi gives three tips for spotting bad statistics:
1) Check for uncertainty - visualizations often overstate certainty to seem objective.
2) See if you can see yourself in the data - national averages may not reflect personal experiences.
3) Consider how the data was collected - finding the source and process helps evaluate reliability.
Managers should be aware of misleading statistics to make accurate decisions, as decisions based on bad data can harm company performance long-term. While statistics can cause skepticism, understanding collection methods allows reliable numbers to still guide policy decisions.
3 ways to spot a bad statistic
This document discusses how to spot bad statistics. It provides three questions to ask: 1) Can you see uncertainty in the data? Many visualizations overstate certainty. 2) Can I see myself in the data? Data needs context about how it relates to people's lives. 3) How was the data collected? It's important to understand how surveys and studies were conducted. Bad statistics can mislead decision making, so it's crucial to evaluate data collection methods and understand limitations to get full context. Statistics are still important for policymaking, but they must be questioned and interpreted carefully.
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This document discusses perspectives on statistics from both pro-stats and anti-stats viewpoints. Pro-stats advocates see statistics as objective measures of societal progress, while anti-stats are skeptical of statistics being elitist or not reflecting everyday experiences. It also outlines key points to consider when looking at statistics, such as understanding uncertainty in data, seeing yourself represented, how data is collected, the importance of sample size for government data, and being wary of manipulated visualizations.
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3 ways to spot a bad statistic
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FROM DATA TO ACTIONA HARVARD BUSINESS REVIEW INSIGHT CE.docx
FROM DATA
TO ACTION
A HARVARD BUSINESS REVIEW INSIGHT CENTER REPORT
Sponsored by
FROM THE HBR.ORG INSIGHT CENTER “FROM DATA TO ACTION” | II© 2014 Harvard Business Publishing. All rights reserved.
FROM DATA TO ACTION
The HBR Insight Center highlights emerging thinking around today’s most important business
ideas. In this Insight Center, we’ll take a closer look at what your data may or may not be telling
you. Tapping researchers and practitioners in data science, marketing, and other fields, we’ll offer
guidelines on making good use of information and turning it into profitable behaviors.
1 You’ve Got the Information, but What Does It Mean?
Welcome to “From Data to Action”
by Andrew O’Connell and Walter Frick
2 What to Ask Your “Numbers People”
By Tom Davenport
3 A Better Way to Tackle All That Data
By Chris Taylor
4 Story-Driven Data Analysis
By Judy Bayer and Marie Taillard
5 Stop Assuming Your Data Will Bring You Riches
By Sunand Menon
7 Nate Silver on Finding a Mentor, Teaching Yourself
Statistics, and Not Settling in Your Career
By Walter Frick
9 Google on Launching an Analytics MOOC and Taking
Data-Driven Actions
By Maureen Hoch
12 Can Your C-Suite Handle Big Data?
By Brad Brown
14 Are You Ready for a Chief Data Officer?
By Thomas C. Redman
16 Don’t Let Data Paralysis Stand between You and Your
Customers
By Harald Fanderl
18 Does Your Company Actually Need Data Visualization?
By Bill Shander
20 To Avoid the Customer Recency Trap, Listen to the Data
By Scott A. Neslin
22 The Metrics Sales Leaders Should Be Tracking
By Scott Edinger
24 To Understand Consumer Data, Think Like an
Anthropologist
By Susan Fournier and Bob Rietveld
26 How Is Big Data Transforming Your 80/20 Analytics?
By Michael Schrage
27 Use Your Sales Force’s Competitive Intelligence Wisely
By Joël Le Bon
28 Can You See the Opportunities Staring You in the Face?
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29 How to Start Thinking Like a Data Scientist
By Thomas C. Redman
31 How a Bathtub-Shaped Graph Helped a Company
Avoid Disaster
By James H. Dulebohn and John Malanowski
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By Jess Neill
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Good data vs bad data: 3 ways to spot a bad statistics
A thin line between good statistics and a bad statistics. This presentation gives three questions that you should ask to your data.
3 ways to spot a bad statistic
The document provides 3 ways to spot bad statistics. The first is to verify if uncertainty is represented in data visualizations which often overstate certainty. The second is to determine if you can see yourself represented in the data. The third is that we need government statistics but must learn to critically evaluate them rather than blindly accepting or rejecting them, as we live in a world of alternative facts where statistics are no longer common ground for debate.
3 ways to spot a bad statistic
This presentation is based on the ted conference of mona chalabi titled "3 ways to spot a bad statistic "
Analysis of Alan Smith TED talk
Alan Smith explores the mismatch between people's perception of their ability to understand statistics and the reality. While many think they have strong statistical skills, people are actually quite poor at intuitive statistics due to factors like individual experiences and media focusing on exceptions. This disconnect is important for managers to understand when making decisions based on consumer data and surveys in diverse markets like India.
Make data more human
Data should be made more human by relating it to real people and understanding the context in which it was collected. To do this, humans must frame the problems, remember that more data is not always better, acknowledge biases in self-reported data, and use stories to convey emotions that data cannot capture on its own. Effective marketing still requires human judgment to discern valuable insights from data and understand how data affects people.
Analysis of “what do you do with all this big data” –ted talk by susan etlinger
The document summarizes key points from a Ted Talk about managing big data. It notes that big data comes from many sources like social media, smartphones, and online activities. While big data can provide insights, it also needs to be interpreted carefully to avoid misinterpretations. Managers need to focus on critical thinking when analyzing big data and consider factors beyond just facts and figures to avoid misleading conclusions. Proper analysis and communication is needed to ensure insights are derived while maintaining public trust in how data is used and interpreted.
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3 ways to spot a bad statistic.
Mona Chalabi gives three tips for spotting bad statistics:
1) Check for uncertainty - visualizations often overstate certainty to seem objective.
2) See if you can see yourself in the data - national averages may not reflect personal experiences.
3) Consider how the data was collected - finding the source and process helps evaluate reliability.
Managers should be aware of misleading statistics to make accurate decisions, as decisions based on bad data can harm company performance long-term. While statistics can cause skepticism, understanding collection methods allows reliable numbers to still guide policy decisions.
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Why should you_love_statistics
This presentation analyses the beautiful TED Talk of Alan Smith on "Why should you love statistics". Gathering the insights and employing those insights is the major task of this presentation.
3 ways to spot a bad statistic
This document discusses perspectives on statistics from both pro-stats and anti-stats viewpoints. Pro-stats advocates see statistics as objective measures of societal progress, while anti-stats are skeptical of statistics being elitist or not reflecting everyday experiences. It also outlines key points to consider when looking at statistics, such as understanding uncertainty in data, seeing yourself represented, how data is collected, the importance of sample size for government data, and being wary of manipulated visualizations.
An Introduction to Data Visualization
Data visualization refers to visually representing data through charts, graphs, and other images to more easily identify patterns and insights. It is an important tool for understanding data, communicating findings to others, and making informed decisions. Effective data visualization requires choosing the right type of visual based on the data, ensuring the data is accurate and from a reliable source, and using the visualization to tell a story or answer key questions. There are many tools available for creating data visualizations, from Excel and Google Sheets for basic charts to more advanced options like Tableau and Photoshop.
Make data more human
Jer Thorp's work focuses on adding meaning and narrative to large amounts of data to help people understand and control the information around them. There are six ways to make data more human: use human insight to frame problems, remember that more data is not always better and can find false correlations, account for human biases and self-deception in data, understand that context is important, embrace that data can help abandon stereotypes, and realize that stories told by robots lack human emotion. These insights are relevant for managers in India because collecting and storing vast amounts of personal data overseas risks privacy violations and data access by foreign governments or companies that could affect a nation's policies.
3 ways to spot a bad statistic
An assignment for an internship on Data Analytics with Managerial Applications under Professor Sameer Mathur, IIM Lucknow
FROM DATA TO ACTIONA HARVARD BUSINESS REVIEW INSIGHT CE.docx
FROM DATA
TO ACTION
A HARVARD BUSINESS REVIEW INSIGHT CENTER REPORT
Sponsored by
FROM THE HBR.ORG INSIGHT CENTER “FROM DATA TO ACTION” | II© 2014 Harvard Business Publishing. All rights reserved.
FROM DATA TO ACTION
The HBR Insight Center highlights emerging thinking around today’s most important business
ideas. In this Insight Center, we’ll take a closer look at what your data may or may not be telling
you. Tapping researchers and practitioners in data science, marketing, and other fields, we’ll offer
guidelines on making good use of information and turning it into profitable behaviors.
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3 ways to spot a bad statistic
The document provides 3 ways to spot bad statistics. The first is to verify if uncertainty is represented in data visualizations which often overstate certainty. The second is to determine if you can see yourself represented in the data. The third is that we need government statistics but must learn to critically evaluate them rather than blindly accepting or rejecting them, as we live in a world of alternative facts where statistics are no longer common ground for debate.
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Alan Smith explores the mismatch between people's perception of their ability to understand statistics and the reality. While many think they have strong statistical skills, people are actually quite poor at intuitive statistics due to factors like individual experiences and media focusing on exceptions. This disconnect is important for managers to understand when making decisions based on consumer data and surveys in diverse markets like India.
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Data should be made more human by relating it to real people and understanding the context in which it was collected. To do this, humans must frame the problems, remember that more data is not always better, acknowledge biases in self-reported data, and use stories to convey emotions that data cannot capture on its own. Effective marketing still requires human judgment to discern valuable insights from data and understand how data affects people.
Analysis of “what do you do with all this big data” –ted talk by susan etlinger
The document summarizes key points from a Ted Talk about managing big data. It notes that big data comes from many sources like social media, smartphones, and online activities. While big data can provide insights, it also needs to be interpreted carefully to avoid misinterpretations. Managers need to focus on critical thinking when analyzing big data and consider factors beyond just facts and figures to avoid misleading conclusions. Proper analysis and communication is needed to ensure insights are derived while maintaining public trust in how data is used and interpreted.
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3 ways to spot a bad statistic
This document discusses how to identify bad statistics. It provides three questions to ask:
1. Question the uncertainty - statistics often overstate certainty, so remember imprecisions.
2. Can I see myself in the data? - Get context by seeing how data changes over time and if it's relevant.
3. How was the data collected? - Find out the methodology to ensure the chart shows everything needed.
While statistics are important, they must be scrutinized carefully to avoid being misled. Asking these questions helps assess statistics properly.
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Big data can provide valuable insights but also risks finding spurious correlations rather than true patterns. With large data sets, the chances of random correlations being identified increase faster than actual valid correlations. Additionally, humans are prone to confirmation bias and seeing patterns where none exist. To maximize insights from big data, it is important to create a statistically literate culture, collect comprehensive high quality data, set clear objectives, leverage algorithms to identify apparent correlations but rely on human judgment to validate findings are not spurious, test hypotheses, and regularly re-evaluate insights on new data.
Similar to Analysis of 3 ways to spot a bad statistic by mona chalabi (20)
FROM DATA TO ACTIONA HARVARD BUSINESS REVIEW INSIGHT CE.docx
FROM DATA TO ACTIONA HARVARD BUSINESS REVIEW INSIGHT CE.docx
Good data vs bad data: 3 ways to spot a bad statistics
Good data vs bad data: 3 ways to spot a bad statistics
Analysis of “what do you do with all this big data” –ted talk by susan etlinger
Analysis of “what do you do with all this big data” –ted talk by susan etlinger
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Nagpur mktg- darpanraj deoghare
- Darpanraj Deoghare is a business development professional with over 8 years of experience in sales, marketing, business planning, and process improvement.
- He is currently seeking a new role and has experience leading marketing teams and developing strategies to enhance business and increase revenue.
- His background includes roles managing marketing operations and business development for educational institutions and developing business in various districts across Maharashtra.
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Analysis of 3 ways to spot a bad statistic by mona chalabi
- 1. Analysis of 3 ways to spot a bad statistic by Mona Chalabi By Darpanraj Deoghare
- 2. Who Is She One of the finest work of her is to find out how many iraqis had been forced from their homes as a result of the war & their needs .
- 3. What is Statistics Sometimes it's hard to know what statistics are worthy of trust. But we shouldn't count out stats altogether ... instead, we should learn to look behind them
- 4. Instances of bad statistics A bill to measure racial inequality which states that government Money should not be used to collect data on racial segregation.
- 5. 3 questions to spot bad statistics Can we see Uncertainty ? Can we see ourselves in data ? How was the data collected ?
- 6. Checking uncertainty in numbers Taking real data set & turning them into hand-drawn visualizations For example, instead of finding out the probability of getting the flu in any given month, we can see the rough distribution .
- 7. lot of data visualizations will overstate certainty, and it works -- these charts can numb our brains to criticism. When you hear a statistic, you might feel skeptical. As soon as it's buried in a chart, it feels like some kind of objective science, and it's not.
- 8. The point of these visualizations is also to remind people of some really important statistical concepts, concepts like averages.
- 9. Can we see ourselves in data ? The point of this isn't necessarily that every single dataset has to relate specifically to you. The point of asking where you fit in is to get as much context as possible. So it's about zooming out from one data point and seeing how it changes over time.
- 10. Insights
- 11. How was the data collected ? You just keep checking everything. Find out how they collected the numbers. Find out if you're seeing everything on the chart you need to see. But don't give up on the numbers altogether, because if you do, we'll be making public policy decisions in the dark, using nothing but private interests to guide us.
- 13. Managers get an understanding of where their companies' survey lacks. They can try to expand their sample space and make it more diverse and bigger which will help make better inferences and lead to better strategies and projects. The main point of any presentation is for people to understand the important facts, and not just numbers. Managers must learn the art of communication and data interpretation. It is important to understand what the numbers are saying.
- 14. Submitted for Internship "Data Analytics " under Professor Sameer Mathur , IIML By, Darpanraj Deoghare
- 15. Thank You