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20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 2

Can ChatGPT provide answers to data science questions to the same standard of humans? Check out this attempt to do so, and compare the answers to those from experts.

20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 2
Image created by Midjourney


7 years ago this month, KDnuggets published an article titled 20 Questions to Detect Fake Data Scientists, written by Andrew Fogg. The following month, KDnuggets editors collectively answered the questions in the subsequent article 21 Must-Know Data Science Interview Questions and Answers.

Looking to utilize ChatGPT in new and exciting ways — to both learn more about ChatGPT itself, and learn about data science interview question topics — we decided to resurrect those same questions on the septennial anniversary of the original, and pose them to ChatGPT.

I will preface this article with the clear statement that all of the answers to the questions in this article have been provided by ChatGPT. Do with that information what you will.

I would encourage readers to compare these answers with those provided by the KDnuggets editors in 2016, in order to see which answers are more thorough, which are more accurate, and which just read better. Feel free to criticize particular answers or the entire project in the comments below.

Now let's have a look to see how well ChatGPT does at answering the following 10 questions that we still agree would be great candidates for detecting fake data scientists applying for a job in your organization. You can find 10 additional questions, the first half, in this previously published article.

11. What is selection bias, why is it important and how can you avoid it?

Selection bias is a type of bias that occurs when a sample of individuals is not selected randomly and therefore does not accurately represent the population it is supposed to represent. This can happen in many ways, such as by selecting individuals based on certain characteristics, only recruiting participants from a certain area, or only including individuals who meet certain criteria.

Selection bias is important because it can lead to inaccurate conclusions and flawed research findings. If a sample is not representative of the population, any inferences or conclusions drawn from the sample may not apply to the population as a whole.

To avoid selection bias, researchers should use random sampling techniques when selecting participants for a study. This can be done by using a random number generator to select participants, or by using a stratified random sampling approach that takes into account certain characteristics of the population. Additionally, researchers should ensure that their recruitment strategies are not biased and that participants are not excluded from the study on the basis of certain characteristics. Researchers should also report their sampling method in detail in the paper.

It is also important to use appropriate statistical methods to adjust for any potential bias in the sample, when analyzing the data.

12. Give an example of how you would use experimental design to answer a question about user behavior.

One example of how experimental design can be used to answer a question about user behavior is through a randomized controlled trial (RCT).

Let's say the question is "Does a new feature on a website increase user engagement?"

An experimental design would be to randomly divide the website's users into two groups: a control group and an experimental group. The control group would continue to use the website as usual, while the experimental group would have access to the new feature.

The researcher would then measure user engagement (e.g. time spent on the website, number of pages visited, number of interactions with the new feature) for both groups over a set period of time. By comparing the engagement data of the two groups, the researcher can determine if the new feature has a significant impact on user engagement.

By using random assignment to form the two groups, we can be sure that any differences observed between the two groups are due to the new feature and not to other factors. Additionally, by using a control group, we can be sure that any differences observed are not due to chance.

This experimental design allows for a causal inference, meaning that the researcher can infer that the new feature caused the change in user engagement, rather than just observing a correlation.

13. What is the difference between "long" and "wide" format data?

In "long" format data, each row represents a single observation and there are several columns that represent different variables, such as the subject ID, the variable of interest and the value of that variable. This format is also known as "stacked" or "melted" data.

For example, a study on height and weight of 10 individuals, in long format, the data would look like this:

Subject ID Variable Value
1 Height 180
1 Weight 75
2 Height 175
2 Weight 80
... ... ...

In "wide" format data, each row represents an individual and there are several columns that represent different variables of interest, and each variable has its own column. This format is also known as "unstacked" data.

For example, the same data as above, in wide format would look like this:

Subject ID Height Weight
1 180 75
2 175 80
... ... ...

The main difference between "long" and "wide" format data is the organization of data, "long" format data is organized by observations and variables, while "wide" format data is organized by observations and variables of interest. Some data analysis and visualization methods may be more suitable for one format over the other, and it is not uncommon to convert between the two formats depending on the analysis needs.

14. What method do you use to determine whether the statistics published in an article (e.g. newspaper) are either wrong or presented to support the author's point of view, rather than correct, comprehensive factual information on a specific subject?

There are several methods that can be used to determine whether the statistics published in an article are either wrong or presented to support the author's point of view, rather than correct and comprehensive factual information on a specific subject. Some of these methods include:

  • Check the sources: Look for the sources of the statistics used in the article and verify their credibility. Are they reputable organizations or individuals? Have they been peer-reviewed or independently verified?
  • Look for context: Make sure to read the article in its entirety and understand the context in which the statistics are being presented. Are they being used to support a specific point of view or agenda?
  • Compare with other sources: Look for other sources that report on the same subject and compare the statistics and information presented in the article to those sources.
  • Check for bias: Look for any signs of bias in the way the statistics are presented. For example, selective reporting of data, using statistics selectively, or presenting data in a misleading way.

Consider Sample Size and Representativeness: If a sample size is small, it may not be representative of the population and the statistics may not be generalizable. Also, if the sample is not selected randomly, it may be biased.

Check for the methodology: Look for the methodology used to collect the data, if it's not described, it's hard to evaluate the accuracy of the statistics.

It is also important to keep in mind that statistics can be misleading if they are not presented in the right context, or if they are used to support a specific point of view or agenda. Therefore, it is crucial to be critical when reading any article that presents statistics, and to always look for additional sources of information to verify the accuracy of the statistics presented.

15. Explain Edward Tufte's concept of "chart junk."

Edward Tufte is a statistician and information designer who has written several influential books on data visualization. One of his key concepts is "chart junk," which refers to the use of non-informative or redundant elements in data visualizations that detract from the message being conveyed. These elements can include things like gridlines, decorative borders, 3D effects, and unnecessary text. Tufte argues that chart junk can make it more difficult for viewers to understand the information being presented, and can lead to confusion and misinterpretation of the data.

Tufte's main argument is that the main goal of data visualization is to present the information clearly and accurately, and that chart junk can be a distraction from this goal. He argues that good data visualization should prioritize the data itself, making it easy to read and understand, and that any additional elements should be used sparingly and only if they serve a clear purpose.

He also emphasizes that data visualization should be simple, clear, and focus on the data and its message, rather than on the design. In his view, effective data visualizations should be able to stand alone, without the need for additional text or annotations.

Tufte's ideas have had a significant impact on the field of data visualization and continue to be widely referenced and applied in areas such as business, data science and journalism.

16. How would you screen for outliers and what should you do if you find one?

There are several methods for screening for outliers, including:

1. Visual inspection: Plotting the data using a graph such as a boxplot or scatter plot can make it easy to identify outliers. Outliers will typically be located far away from the rest of the data points.

2. Z-score method: This method calculates the standard deviation and mean of the data, and then assigns a score (z-score) to each data point. Data points with a z-score greater than a certain threshold (usually 3 or -3) are considered outliers.

3. Interquartile range (IQR) method: This method calculates the interquartile range (the difference between the 75th and 25th percentiles) and assigns a lower and upper bound to the data. Any data points that fall outside of these bounds are considered outliers.

4. Mahalanobis Distance method: This method calculates the Mahalanobis distance of each data point from the mean of the data. The Mahalanobis distance is a measure of how far a data point is from the mean in terms of standard deviations. Data points with a Mahalanobis distance greater than a certain threshold are considered outliers.

Once outliers have been identified, it is important to consider their impact on the data and the analysis. There are several ways to handle outliers, depending on the context of the data and the research question:

1. Exclude outliers: This is the most common approach, particularly if the outliers are the result of measurement error or data entry errors.

2. Keep outliers and report them: If the outliers are real data points and are believed to be informative, it is important to keep them in the data set and report them in the analysis.

3. Transform the data: In some cases, outliers can be transformed using a log or square root transformation, which can reduce the impact of the outliers on the data.

4. Investigate outliers: Outliers may indicate a problem with the data collection or measurement process, and it is important to investigate and understand why they occurred.

It's important to consider the nature of the data, the study's objectives and the context of the data before deciding to exclude or keep outliers, and to report the treatment of the outliers in the final report.

17. How would you use either the extreme value theory, Monte Carlo simulations or mathematical statistics (or anything else) to correctly estimate the chance of a very rare event?

Extreme Value Theory (EVT) can be used to estimate the probability of rare events by modeling the extreme tails of a probability distribution. This can be done by fitting a distribution, such as the Gumbel or Generalized Pareto distribution, to the extreme values of a dataset.

Monte Carlo simulations can also be used to estimate the probability of a rare event by generating many random samples from a probability distribution and counting the number of times the event of interest occurs. This can be useful when the underlying distribution is complex or unknown.

Mathematical statistics can be used to estimate the probability of a rare event by using methods such as maximum likelihood estimation or Bayesian inference. These methods use mathematical models and algorithms to estimate the probability of an event based on the available data.

Another approach is to use machine learning method to model the rare events and predict the chances of it.

It is important to note that, the accuracy of the estimation depends on the quality and quantity of data, the assumptions made about the underlying distribution, and the method used for estimation.

18. What is a recommendation engine? How does it work?

A recommendation engine is a system that uses machine learning algorithms to recommend items to users based on their past behavior and preferences. It is often used in e-commerce, media and entertainment, and social media to provide personalized recommendations to users.

There are two main types of recommendation engines: content-based and collaborative filtering.

Content-based recommendation engines use the characteristics or attributes of the items to recommend similar items to a user. For example, if a user likes movies with a certain actor, the engine might recommend other movies with that actor.

Collaborative filtering, on the other hand, uses the behavior of users to recommend items. It looks at the interactions of users with items, such as rating or purchasing history, and finds similar users to recommend items that they liked. There are two types of collaborative filtering: user-based and item-based. User-based CF recommends items to a user based on their similarity to other users, item-based CF recommends items based on their similarity to the items a user has previously interacted with.

Both types of recommendation engines use machine learning algorithms, such as k-NN, SVD, and matrix factorization, to analyze the data and make recommendations. They also use techniques like regularization and cross-validation to avoid overfitting of the models.

Overall, recommendation engines use data on user behavior and item characteristics to make personalized recommendations for each user. They are widely used in different industries and help to increase user engagement and sales.

19. Explain what a false positive and a false negative are. Why is it important to differentiate these from each other?

A false positive is a situation where a test result indicates that a particular condition is present when it is actually not. For example, in medical testing, a false positive would occur if a patient is told they have a certain disease when they do not actually have it.

A false negative, on the other hand, is a situation where a test result indicates that a particular condition is not present when it actually is. For example, a false negative in medical testing would occur if a patient is told they do not have a certain disease when they actually do have it.

It is important to differentiate between false positives and false negatives because they have different implications. A false positive could lead to unnecessary treatments, procedures, or further testing, while a false negative could result in a delay or failure in diagnosis and treatment.

In the field of machine learning, the difference between false positive and false negative is also important. The false positive rate and false negative rate are two commonly used metrics to evaluate the performance of a classifier, which can help to understand the trade-off between accuracy and recall. For example, in a medical diagnosis problem, if the false negative rate is too high, it means that many patients with the disease will be misdiagnosed, which can lead to serious consequences.

Different applications have different requirements for the balance between false positives and false negatives, depending on the stakes of the problem, the costs of each type of error, and the desired level of confidence in the results.

20. Which tools do you use for visualization? What do you think of Tableau? R? SAS? (for graphs). How to efficiently represent 5 dimension in a chart (or in a video)?

There are many tools that can be used for data visualization, and the choice of tool will depend on the specific requirements of the project and the experience of the user. Some popular tools for data visualization include:

  • Matplotlib and Seaborn: These are Python libraries that are widely used for creating static, 2D plots and visualizations. They are easy to use, have a large community, and can be integrated with other Python libraries such as Pandas.
  • D3.js: D3.js is a JavaScript library for creating interactive, web-based visualizations. It is widely used for creating complex, interactive visualizations such as maps, scatterplots, and line charts.
  • Tableau: Tableau is a powerful data visualization tool that allows users to easily create interactive, visually appealing visualizations. It is widely used by data analysts and business intelligence professionals. Tableau is very powerful but also quite expensive.
  • R: R is a programming language that is widely used for data analysis and visualization. It has a large number of packages for data visualization, such as ggplot2 and lattice, which are widely used for creating static, 2D plots and visualizations.
  • SAS: SAS is a software suite that is widely used for data analysis and visualization. It has a wide range of data visualization options, including scatter plots, line plots, and heat maps, as well as advanced visualization options such as 3D plots and animations.

To represent 5 dimension in a chart, one of the options is to use parallel coordinates plots, which allow displaying multivariate data by plotting each variable as a parallel axis, and representing each observation as a polyline connecting the values on each axis. Another option is to use a scatterplot matrix (also known as a pair plot) which allows to show all the pairwise relationships between variables in a matrix format, in this case one can use different colors, shapes or sizes to represent the different dimensions.

Another option is to use a parallel coordinates plot with brushing and linking, which allows to interactively explore the data by highlighting observations and linking to other views.

It is also possible to represent 5 dimension in a video, one way is to use animation to show how the data changes over time and another way is to use interactive visualizations that allow users to explore the data by interacting with the visual elements.

It is important to note that, representing 5 dimension in a chart or in a video can be challenging, as it requires careful design choices to effectively convey the information and avoid overwhelming the user with too much data.

That is now all of the 20 questions from the original publication. Hopefully we all learned something interesting from the content of the answers or the process of asking ChatGPT to provide them.

Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.