2) Statistics is very easy to be misinterpreted or think that it is misled because of sampling bias when gathering data, maybe because of using different techniques and proportions than what the investigation and use of statistics require. There could be selective biases like cherry picking data and overgeneralisation of one data point to make it seem much bigger than it is. Manipulation of the visuals and graphs. An example of this is:

The Literary Digest, a well-known journal, incorrectly predicted Alfred Landon's election victory. Despite a track record of accuracy, the 1936 forecast failed because of skewed factors. 1) selective bias- The poll used telephone and vehicle registration databases to reach out to people in a specified socioeconomic category. 2) Furthermore, there was a bias because Landon supporters were more likely to participate in the poll, resulting in a major distortion of general sentiment. In the end, Franklin D. Roosevelt won convincingly, emphasising the need of extensive and fair polling methodology.

3) It is not justifiable to use statistics with the intention to mislead others as it can cause many problems such as deception, incorrect conclusions and low reliability and confidence which is not fair and honest.

Statistics can be mislead due to the different types of data collection. Demographics need to be cross checked and verified effectively to not be mislead by statistical data.

It is incorrect to use statistics to mislead others with data provided since people make decisions and analyse situations through statistics and a faulty statistical report can cause more damage than benefits. An example of misleading statistics that caused deceptions is the statistics China uploaded about their Covid-19 cases in 2020. They intentionally deceived other countries by hiding the actual number of cases and reducing them while providing statistics. This was damaging for China since, it portrayed to countries that they cannot trust their government and there were data based conflicts globally.

Cherry-picking Data: To give the appearance that crime rates have dramatically grown during the current administration, a political candidate may use crime figures from a certain time period. The candidate might skew the statistics to fit their story by utilising a small timeframe or disregarding past tendencies.

Correlation versus Causation: Let's say a study discovers a positive relationship between the quantity of ice cream sold and the frequency of drownings in swimming pools. Drawing the conclusion that increasing ice cream purchases cause an increase in drownings would be erroneous. As warm weather really affects both variables, it is important to take confounding factors into account.

2) There are many factors that lead to misleading statistics such as selective bias and the manipulation of graphs and visuals this is widely used by politicians and the government to manipulate the general public into thinking that the country has taken up a good path although in reality these graphs and data is manipulated to make it look better to the public's eye.

3) Personally I think it is not justified to use statistics to mislead others this does not only provide false information on important topics but it also brings down the credibility of the source that is providing this data. Half-truths are not fair towards the general public because the blind faith they have on this data and statistics can cause a lot of false information to dpread in the world.

2) I think that it is very easy to be mislead by statistics, as they can be manipulated by anyone, and as humans are cognitive misers- we would not look into statistics we see, and be content with the values we see without feeling the need to further investigate. According to DataPine, there are many examples of misleading COVID statistics- some countries felt the need to look better by manipulating the statistics based on the death rate from COVID 19.

3) No, deliberately using data to mislead someone is unethical and dishonest. It breaks trust, gives the wrong idea about the reality of a situation, and can have serious consequences- especially in business and bigger organizations. It hampers informed decision-making.

2) It is relatively easy to be misled by statistics if one is not vigilant and critical in their interpretation. Statistics can be manipulated or presented in a biased manner, leading to false conclusions. Common pitfalls include selecting a non-representative sample, misusing statistical tests, or presenting data without proper context. Additionally, the misinterpretation of correlation as causation and reliance on small sample sizes can contribute to erroneous conclusions. Therefore, statistical literacy and a nuanced understanding of data are essential for individuals to navigate the complex landscape of statistical information.

3) It is never justifiable to purposely use statistics to mislead others. Deliberate manipulation of statistical information for deceptive purposes, known as statistical deception, is considered unethical. Ethical guidelines in research stress transparency, honesty, and accuracy in reporting findings. Intentionally distorting statistics can erode trust in data, compromise the integrity of research, and have far-reaching consequences. Maintaining the credibility of statistical information is crucial for informed decision-making, policy development, and public trust in various domains, including science, business, and governance. Ethical considerations dictate that statisticians and researchers should prioritise truthfulness and integrity in their use and presentation of statistical data.

2.Those who lack statistical literacy or critical thinking abilities are easily tricked by misleading statistics. Inaccurate conclusions can arise from the selective use of data or the presentation of statistics out of context. Using biassed samples, relying on small sample sizes, and mistaking correlation for causation are common mistakes. People need to be sceptical of methodology, sources, and potential biases when consuming statistical information in order to avoid being mislead in a world where data is abundant.

3.Using statistics to intentionally mislead people is never ethically acceptable. Such activities have far-reaching effects, compromise the reliability of information sources, and compromise the integrity of data. Transparency, honesty, and accuracy in the communication of research findings are prioritised by ethical guidelines in data reporting and research. Deliberate statistical manipulation, also referred to as statistical deception, is widely condemned and can happen in a variety of settings, including political discourse and advertising. Ensuring that information is used responsibly and truthfully in decision-making processes is a shared obligation of society, as much as it is for statisticians and researchers to uphold the ethical use of statistics.

Mathematics and statistics have a complicated relationship. Because of its reliance on mathematical concepts and procedures, statistics is frequently seen as a branch of mathematics. Statistics, on the other hand, has emerged as a distinct science with its own theories, methodology, and applications. This has sparked controversy over whether statistics should be considered a part of mathematics or a separate discipline.

2) Statistics can be deceptive, resulting in inaccurate conclusions, poor decision-making, and a false sense of certainty in particular views or assumptions. Misuse of statistics happens when a statistical argument claims a falsehood, whether mistakenly or on purpose for the perpetrator's benefit. Misleading statistics can provide people with inaccurate information that deceives them rather than informs them, and when taken out of context, they can lead to inaccurate conclusions. Statistics can be misleading in a variety of ways, including selective bias, inadequate sample size, inaccurate correlations and causations, and the use of manipulative graphs and visualisations since most viewers tend to accept information as credible when presented with statistics without verifying the information for themselves.

3)In my opinion, using statistics to deceive others is never justified. Statistics that are misleading might be used to build a false narrative about an issue, influencing public opinion and decision-making. This can be observed in advertising, politics, and news, as inaccurate facts are utilised to persuade the public to support a specific product, candidate, or cause. Misleading data can be used to conceal the truth about a product's, service's, or idea's usefulness or performance, making it difficult for consumers to make informed decisions. Presenting inaccurate statistics can assist individuals or organisations to avoid accountability for their actions or decisions by shifting the focus away from the underlying issues and towards the misleading facts.

2)First of all, sampling bias happens when a study's sample isn't representative of the total population, which makes it impossible to generalise the findings. Although this logical mistake is frequently made, correlation does not imply causation, which can lead to incorrect assumptions about cause-and-effect relationships. By altering the way that data is visually represented, misleading graphs and charts have the power to skew perceptions. Biassed interpretations can also result from cherry-picking data, which is the selective presentation of information to favour a particular position while disregarding contradicting information.

3) It is unethical and unacceptable to purposefully mislead others with data. Intentionally tampering with data or statistical analysis compromises the reliability of scientific findings, and can have detrimental effects on understanding. Transparency, honesty, and the appropriate application of statistical methods are prioritised by ethical rules and standards. These values forbid deception of others by the use of statistics, and those caught doing so risk moral and legal consequences.

2. I think it is very easy to be misled by statistics due to inherent biases. One example of this is people often assuming that correlation means causation. Other examples include biases such as the framing effect where stats can be framed in a particular way to prove a person's main argument

3. I don't believe it is ethically justifiable to use statistics to mislead people since it can cause harm to the individual. For example, in the medical field, if a company frames statistics in a certain way to sell their medicines which aren't actually very effective, it could cost people their lives.

2) Although statistics is an effective tool for drawing conclusions from data, there is always a chance that results will be misinterpreted. Critical interpretation is crucial because incorrect interpretations can be caused by biased data selection, selective sampling results, correlation-causation errors, small sample size constraints, and misleading visual representations.

3) When statistics tend to be intentionally misused, it can have a negative ethical impact on society and the economy, reduce trust, compromise scientific integrity, compromise one's reputation, and interfere with democratic processes. To ensure responsible and credible use of statistical tools, an ethical approach involves transparency, caution in data representation, and a commitment to truthfulness.

Mathematics, as a broader field, involves the study of abstract structures, patterns, and relationships using logic and deductive reasoning. It encompasses a wide range of areas, including algebra, geometry, calculus, and number theory. In contrast, statistics primarily deals with the collection, analysis, interpretation, presentation, and organisation of data. While statistics relies on mathematical principles, it is more focused on the practical application of these principles to real-world problems.

The distinction between mathematics and statistics is somewhat artificial and varies across educational systems. In many cases, they are intertwined, with statistics courses often incorporating mathematical concepts, and mathematical research frequently involving statistical methods.

Regarding the potential for being misled by statistics, it's crucial to recognise that statistics can be manipulated or misinterpreted to convey a particular narrative.