A common mistake in statistics is to deduce a cause-and-effect relationship between two variables solely on the basis of an observed association or correlation between them.

One of the most famous examples is the Pirates versus Global Warming used as a parody by the author of the Flying Spaghetti Monster to debunk other misleading graphs.

I his example Somalia has "the highest number of pirates and the lowest carbon emissions of any country".

Conclusion

This is an important lesson for all data scientists in our fight against misinformation.

sources:

https://en.wikipedia.org/wiki/Flying_Spaghetti_Monster

https://commons.wikimedia.org/wiki/File:PiratesVsTemp(en).svg

In a survey conducted in September 2020 across India, ** percent of the respondents believed cosmetic products and services to be the main source of misleading advertisements in the country. On the other hand, ads related to banking and financial services were found to be the least deceptive.

Descriptive statistics of the other variables.

Descriptive statistics (mean score and standard deviation; the percentage for ‘male’) of the demographic characteristics.

Model summary for the multiple linear regression model with percentage as the dependent variable.

A study held in early 2024 found that more than a third of surveyed consumers in selected countries worldwide had witnessed false news about politics in the week running to the survey. Suspicious or false COVID-19 news was also a problem. False news False news is often at its most insidious when it distorts or misrepresents information about key topics, such as public health, global conflicts, and elections. With 2024 set to be a significant year of political change, with elections taking place worldwide, trustworthy and verifiable information will be crucial. In the U.S., trust in news sources for information about the 2024 presidential election is patchy. Republicans and Independents are notably less trusting of news about the topic than their Democrat-voting peers, with only around 40 percent expressing trust in most news sources in the survey. Social media fared the least well in this respect with just a third of surveyed adults saying that they had faith in such sites to deliver trustworthy updates on the 2024 election. A separate survey revealed that older adults were the least likely to trust the news media for election news. This is something that publishers can bear in mind when targeting audiences with updates and campaign information. Distorting the truth: the impact of false news Aside from reading (and potentially believing) false information, consumers are also at risk of accidentally sharing false news and therefore contributing to its spread. One way in which the dissemination of false news could be stemmed is by consumers educating themselves on how to identify suspicious content, however government intervention has also been tabled. Consumers are split on whether or not governments should take steps to restrict false news, partly due to concerns about the need to protect freedom of information.

a, Frequently, variation in data from across the sciences is characterized with the arithmetic mean and the standard deviation SD. Often, it is evident from the numbers that the data have to be skewed. This becomes clear if the lower end of the 95% interval of normal variation, - 2 SD, extends below zero, thus failing the “95% range check”, as is the case for all cited examples. Values in bold contradict the positive nature of the data. b, More often, variation is described with the standard error of the mean, SEM (SD  =  SEM · √n, with n  =  sample size). Such distributions are often even more skewed, and their original characterization as being symmetric is even more misleading. Original values are given in italics (°estimated from graphs). Most often, each reference cited contains several examples, in addition to the case(s) considered here. Table 2 collects further examples.

Hi Folks,

Let's understand the importance of Data Visualization.

Here below, we have four different data sets and they are paired in the sense of x and y.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F4f6c696e3ad5e2c887b01a0bdd14b355%2Fdata_set.png?generation=1685190700223447&alt=media" alt="">

Next let's calculate some descriptive statistics such as mean, standard deviation and correlation of each variables.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F14765ba12bdc18b8ff67cb6a9f2d7c7a%2Fstatistics.png?generation=1685192394142325&alt=media" alt="">

After examining the descriptive statistics the above four data sets have nearly identical or similar simple descriptive statistics.

However, when we graphically plot the datasets on scatter plot, we can see the difference that these 4 datasets looks very different.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2Fdbccf9dc638d3de28930b9f660e5f5a4%2Fgarph.png?generation=1685191588780934&alt=media" alt="">

Data 1 has a clear linear relationship, Data 2 has a curved relationship that is not linear, Data 3 has a tight linear relationship with one outlier and Data 4 has a linear relationship with one large outlier.

Such datasets are known as Anscombe's Quartet

Anscombe's quartet is a classic example of the importance of data visualization.

Anscombe's quartet is a set of four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphically represented. Each dataset consists of eleven (x,y) points.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12425689%2F2b964d437afe17db949c57988b5fba05%2Fanscombes_quartet.png?generation=1685192626504792&alt=media" alt="">

Anscombe's quartet illustrates the importance of plotting data before we analyze it. Descriptive statistics can be misleading, and they can't tell us everything we need to know about a dataset. Plotting the data on charts can help us to understand the shape of the distribution and to identify any outliers.

Slovenia - Perceived independence of the justice system: Very bad was 16.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Slovenia - Perceived independence of the justice system: Very bad - last updated from the EUROSTAT on November of 2025. Historically, Slovenia - Perceived independence of the justice system: Very bad reached a record high of 32.00% in December of 2016 and a record low of 14.00% in December of 2023.

Sweden - Perceived independence of the justice system: Very bad or fairly bad was 15.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Sweden - Perceived independence of the justice system: Very bad or fairly bad - last updated from the EUROSTAT on December of 2025. Historically, Sweden - Perceived independence of the justice system: Very bad or fairly bad reached a record high of 18.00% in December of 2017 and a record low of 12.00% in December of 2023.

A survey conducted in December 2020 assessing if news consumers in the United States had ever unknowingly shared fake news or information on social media found that 38.2 percent had done so. A similar share had not, whereas seven percent were unsure if they had accidentally disseminated misinformation on social networks.

Fake news in the U.S.

Fake news, or news that contains misinformation, has become a prevalent issue within the American media landscape. Fake news can be circulated online as news stories with deliberately misleading headings, or clickbait, but the rise of misinformation cannot be solely accredited to online social media. Forms of fake news are also found in print media, with 47 percent of Americans witnessing fake news in newspapers and magazines as of January 2019.

News consumers in the United States are aware of the spread of misinformation, with many Americans believing online news websites regularly report fake news stories. With such a high volume of online news websites publishing false information, it can be difficult to assess the credibility of a story. This can have damaging effects on society in that the public struggled to keep informed, creating a great deal of confusion about even basic facts and contributing to incivility.

France - Perceived independence of the justice system: Very bad or fairly bad was 34.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - Perceived independence of the justice system: Very bad or fairly bad - last updated from the EUROSTAT on November of 2025. Historically, France - Perceived independence of the justice system: Very bad or fairly bad reached a record high of 39.00% in December of 2016 and a record low of 29.00% in December of 2022.

Explore the intricacies of banana exports by various countries from 1994 to 2005 in this data visualization challenge. The dataset, derived from the United States Food and Agriculture Organization (FAO), presents an opportunity to uncover trends and dynamics in banana trade during this period.

Your challenge is to reimagine the original graph, which suffered from misleading date ranges, obscured country levels, incorrect color coding, and visual overload. Can you create a 2-dimensional line chart that effectively visualizes the export dynamics between countries and years? Your goal is to enhance data comprehension and usability, providing a clearer understanding of the banana export trends.

Are you up for the challenge of reimagining?

Czech Republic - Perceived independence of the justice system: Fairly bad was 25.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Czech Republic - Perceived independence of the justice system: Fairly bad - last updated from the EUROSTAT on November of 2025. Historically, Czech Republic - Perceived independence of the justice system: Fairly bad reached a record high of 32.00% in December of 2016 and a record low of 20.00% in December of 2023.

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

Italy - Perceived independence of the justice system: Very bad or fairly bad was 54.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Italy - Perceived independence of the justice system: Very bad or fairly bad - last updated from the EUROSTAT on November of 2025. Historically, Italy - Perceived independence of the justice system: Very bad or fairly bad reached a record high of 61.00% in December of 2016 and a record low of 46.00% in December of 2019.

In a survey conducted in May 2025, journalism was rated the most positively by U.S. adults, with 54 percent describing it as very or somewhat favorable. Social media followed with 49 percent favorable, though a notable share of respondents also held negative views. The news media and the press were rated less positively, at 47 and 46 percent, respectively. Overall, the findings suggest stronger confidence in journalism compared to other media institutions.

The decoupling of control and forwarding layers brings Software-Defined Networking (SDN) the network programmability and global control capability, but it also poses SDN security risks. The adversaries can use the forwarding and control decoupling character of SDN to forge legitimate traffic, launching saturation attacks targeted at SDN switches. These attacks can cause the overflow of switch flow tables, thus making the switch cannot forward benign network traffic. How to effectively detect saturation attack is a research hotspot. There are only a few graph-based saturation attack detection methods. Meanwhile, the current graph generation methods may take useless or misleading information to the attack detection, thus decreasing the attack detection accuracy. To solve the above problems, this paper proposes TITAN, a bidirecTional forwardIng graph-based saturaTion Attack detectioN method. TITAN defines flow forwarding rules and topology information, and designs flow statistical features. Based on these definitions, TITAN generates nodes of the bi-forwarding graph based on the flow statistics features and edges of the bi-forwarding graph based on the network traffic routing paths. In this way, each traffic flow in the network is transformed into a bi-directional forwarding graph. Then TITAN feeds the above bidirectional forwarding graph into a Graph Convolutional Network (GCN) to detect whether the flow is a saturation attack flow. The experimental results show that TITAN can effectively detect saturation attacks in SDNs with a detection accuracy of more than 97%.

A 2025 survey found that around one in four adults in the United States actively avoided news related to sports, followed by entertainment (18 percent) and lifestyle (17 percent). In contrast, health was the least avoided news topic, with just four percent of respondents saying they ignored it.

Hungary - Perceived independence of the justice system: Very bad or fairly bad was 39.00% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Hungary - Perceived independence of the justice system: Very bad or fairly bad - last updated from the EUROSTAT on November of 2025. Historically, Hungary - Perceived independence of the justice system: Very bad or fairly bad reached a record high of 39.00% in December of 2024 and a record low of 28.00% in December of 2017.