3 ways to spot a bad statistic
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Presentation on " ANALYSIS OF TED TALK BY MONA CHALABI ON 3 WAYS TO SPOT A BAD STATISTIC" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
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There are three kinds of lies
This document discusses how statistics can be misleading and manipulated. It provides examples of selection bias, such as only surveying a non-representative sample. Other ways statistics can be misleading include using biased questions, asking the wrong question, misleading graphs, implying causation from correlation, making results seem more precise than they are, and making up statistics. The document encourages critically analyzing data and graphs by checking for correct information, potential influence attempts, and proper scale usage. It also discusses how to manually manipulate real data and graphs to draw misleading conclusions.
Misuses of statistics
Statistics can be misused in several ways: by using small, non-random samples; reporting averages that don't accurately represent the data; changing values to misrepresent facts; making claims without proper context or comparison; implying connections that aren't supported; or using misleading visuals. Common statistical misuses include using small samples to draw broad conclusions, citing a mean when the median is more relevant, changing a percentage to an absolute value to distort perceptions, making claims without a baseline for comparison, implying causal relationships not supported by evidence, and graphs that mislead viewers.
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The document discusses the proper and improper uses of statistics. It explains how statistics can be used to describe data, compare data sets, determine relationships, and test hypotheses. However, it also outlines several ways statistics can be misused, such as to sell ineffective products, prove false claims, or induce fear in audiences. Specifically, it describes how using small, biased, or inconvenient samples, ambiguous averages, detached statistics without comparisons, implied connections without proof, misleading graphs, and biased survey questions can distort the truth.
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This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
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This document discusses how statistics can be misleading and manipulated. It provides examples of selection bias, such as only surveying a non-representative sample. Other ways statistics can be misleading include using biased questions, asking the wrong question, misleading graphs, implying causation from correlation, making results seem more precise than they are, and making up statistics. The document encourages critically analyzing data and graphs by checking for correct information, potential influence attempts, and proper scale usage. It also discusses how to manually manipulate real data and graphs to draw misleading conclusions.
Misuses of statistics
Statistics can be misused in several ways: by using small, non-random samples; reporting averages that don't accurately represent the data; changing values to misrepresent facts; making claims without proper context or comparison; implying connections that aren't supported; or using misleading visuals. Common statistical misuses include using small samples to draw broad conclusions, citing a mean when the median is more relevant, changing a percentage to an absolute value to distort perceptions, making claims without a baseline for comparison, implying causal relationships not supported by evidence, and graphs that mislead viewers.
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Analyzing Real Time News
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Authors: Marco Fusi @marco_fusi, Raffaele Lorusso @rlorusso76
Analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
Top 5 tips on how to learn statistics more effectively
In this infographic, you will go through how to learn statistics more effectively than ever before. Have a look at this infographic to start learning statistics
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This document discusses using Twitter data to predict breakpoints in financial markets. It describes an experiment that analyzed tweets to measure context change, which correlates to market volatility. When a trading algorithm stopped trading after Twitter alerts, its profits increased from 0.56% to 1.27%, showing Twitter can predict breakpoints 4-6 minutes before they occur. The results demonstrate a link between social media information on Twitter and upcoming changes in foreign exchange rates.
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Google searches for "amazon.com" do not significantly predict changes in Amazon's stock price, contrary to previous research finding a link between web searches and company performance. The best predictors of Amazon's stock price are the overall stock market as measured by the Russell 3000 index and the unemployment rate, indicating the economy overall has more influence than competitor performance or search volume. Seasonality is also a factor, with Amazon's price negatively impacted during the holidays in recent years when sales have fallen below expectations.
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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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- Statistics involves collecting, analyzing, and presenting quantitative data. It is used across many fields including business.
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Mona Chalabi gives three tips for spotting bad statistics:
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3) Consider how the data was collected - finding the source and process helps evaluate reliability.
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This document discusses how to spot bad statistics and provides tips for evaluating statistical claims. It notes that statistics are an important tool but can also be misleading if not examined carefully. Some key things to consider about statistics are who conducted the study, what the statistics are measuring, who was surveyed, and how they were surveyed. When evaluating statistical claims, it's important to consider how the data was collected, if your own experiences are represented, and if the study accounts for uncertainty. The source of information and method of data collection can also impact the accuracy of statistics. Statistics can be helpful for market research and product design but advertisers may also use misleading statistics to influence consumers.
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"This comprehensive SPSS guide covers essential topics in data analysis and statistical research. Key contents include:
Missing Data: Understanding and handling data gaps (Page 2)
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Interpretation of Output: A guide to exploring and interpreting SPSS outputs (Page 8)
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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
- 2. KEY INSIGHTS
- 4. BUT, DOWE TRUST STATISTICS? 40% OF AMERICANS DON’T TRUST GOVERNMENT STATISTICS
- 5. VARIED VIEWSON STATISTICS SOME SAY: Statistics are crucial, we need them to make sense of society as a whole in order to move beyond emotional anecdotes and measure progress in an objective way. WHILE OTHERS SAY: Statistics are elitist, maybe even rigged; they don't make sense and they don't really reflect what's happening in people's everyday lives.
- 7. 1.CANYOU SEE UNCERTAINT Y Many a times there may be a lot of variations in the data set itself. Thus taking the average of such a data set can lead to very misleading conclusions, So whenever we see a statistic, the first step is to heck for the uncertainty in the data.
- 8. SMALL SAMPLESIZE If the sample size used to predict a result is too small, then the chances are that the statistic wrong. So another way to check for uncertainty is by observing the size of sample.
- 9. 2.CAN ISEE MYSELF IN THE DATA Whenever we see a statistic, we shall try to see if we fit into the data. Even if we cannot directly fit into the dataset we shall try to get as much context as possible. We shall try to zoom out from one point and see the variation of statistic for various sections of the dataset.
- 10. 3. HOWWAS THE DATA COLLECTED? Statisticians many times tend to overlook one fact over another and that can lead to severely misleading results. We shall look into what data was actually collected in a survey and then also investigate which factor in the survey was given precedence over which factor As the precedence of factors can bend the statistic towards either way.
- 12. Whenever a manager is faced with a statistic, he shall look into variance in the data set used and see if there is skew in it. He shall always ask his analysts to never go by the average of the data, as it can lead to very misleading conclusions.
- 14. ENSURETHAT THESOURCE IS RELIABLE ANDGOOD. He shall ensure that his analysts are given good data source to create statistics. A good data set must have the following properties:- 1.A big sample size 2.Contains all the data the valid parameters that may affect the result.