What are the best ways to ensure statistical graphics are accurate and reliable?

It is debatable sometimes what type of graph to outcast to audiences but studying the data should give a fair idea what to use and represent there are few tricks I use such as say for pie charts if things seems to be a proportion we use that. For bar graphs we check for values that does not seem like a fraction then we represent it.

Bar charts are used for making comparisons providing an way to showcase differences. For example they can display the distribution of goals scored by players in a season. On the hand line charts are commonly utilized to track progress or regression, over time, such as monitoring a teams performance throughout a competition. Scatter plots reveal how variables interact with each other and help identify relationships like the correlation between possession percentage and winning percentage. For presenting data it is crucial to select the appropriate type of graphic that truly reflects the authenticity of the data. The key objective is to enlighten than confuse and to provide information that's informative rather, than misleading.

The foundation of any visualization relies on the precision of its scales and axes which provide the framework, for conveying stories through data. In sports analytics scales and axes serve a role beyond markers. They offer context and comprehension to the information being presented. The labels on the axes serve as a guide for determining the direction and magnitude of the data. It is vital to choose units and intervals that align with the used language in sports. If we truncate or modify the axes it can diminish the variations within the data. Therefore using scales and axes accurately is not, about correctness but also reflects our dedication to presenting reliable and easily understandable information.

Labels and titles serve as guides that help us understand and make sense of information. They act as markers, along the journey of data transforming it from numbers into a captivating story. Labels require precision and attention to detail when providing annotations. On the hand engaging titles play the role of messengers for your content capturing the essence of the information being presented. A legend is like a playbook that helps us decipher the elements in a dataset. Symbols, colors and lines, ensuring there is no room for misinterpretation. Mistakes such as typos or missing information, in data visualization can undermine the credibility of our analysis like mistakes on the pitch.

In the world of presenting data, colors and shapes are not, about looks. They play a role in directing attention conveying information and aiding memory. Use colors strategically by using contrasting shades to distinguish between data categories making each one stand out. You can also use shades of the color to represent various values within a single category. Similarly shapes can serve as markers to indicate types of data or strategies each with its importance and role, in the visual presentation. It's important to avoid using colors that might overwhelm viewers or clash with each other as this can disrupt the overall harmony of the data. The ultimate goal is to create an cohesive presentation that leaves a lasting impression.

Checking the quality and accuracy involves verifying the data sources for credibility and ensuring the data collection methods are sound and unbiased. It's important to clean and preprocess the data to remove errors, outliers, or missing values that could skew results. Consistency in data measurement and scales is crucial to avoid misinterpretations. Analyzing the distribution of data and understanding its characteristics can also help in choosing the right type of graphic representation. Regularly cross-checking the data against other sources or benchmarks can further ensure its reliability. High-quality data is the foundation of trustworthy statistical graphics, making thorough data validation an essential step in the process.

Adherence to ethical principles and standards involves presenting data honestly and objectively, without manipulating or cherry-picking data to mislead or confirm a bias. It's essential to use appropriate scales and axes that accurately reflect the data's range and distribution, avoiding distortions or exaggerations. Transparency in methodology and data sources is key. Ethical practice also includes acknowledging limitations and uncertainties in the data or analysis. Additionally, providing clear, accessible explanations alongside graphics helps a diverse audience correctly interpret the information. Upholding these ethical standards is crucial to maintaining trust and integrity in statistical communication.

I completely agree, just want to make an addition "Statistics do not imply accuracy up to 100%, its whole purpose is figure out a reasonable and workable estimate." Nothing more and nothing less.

In terms of accuracy one suggests to include things such as outliers in the calculation. This gives a broad picture of what is in the datasets. Avoiding bias is significant too.

Consider a graphic as an iconic message to community. Just keep in mind that you are making a "visual phrase" that should be easy understandable by a wide range of people.