How can you avoid making assumptions in data analysis?

To avoid making assumptions in data analysis, start by clearly defining your objectives and using random sampling techniques. Scrutinize the data collection process, check for missing data, and verify data accuracy. Employ exploratory data analysis (EDA) to uncover patterns, and consider alternative hypotheses. Test assumptions underlying statistical methods and employ cross-validation for predictive models. Finally, document your process, seek peer review, and remain mindful of cognitive biases to maintain objectivity and enhance the reliability of your analysis.

Initiating data analysis with a definitive question is essential for precise targeting. This habit ensures that we directly address the root cause of the issue at hand. Until there's a confirmed cause-and-effect relationship between the data and the core problem, treating correlations as assumptions is prudent to avoid misinterpretations and maintain the integrity of any potential conclusions.

In data analysis, it is important to avoid circular reasoning. One way to do this is to avoid stating the research question as simply the absence of the thing you are suggesting. For example, instead of saying: "The research problem is that there is no cure for cancer." You could say: "The research question is to develop a new treatment for cancer that is more effective than existing treatments." By defining the research question in this way, you are not assuming the very thing you are trying to prove. Instead, you are stating a clear and specific problem that can be tested with data.

Science progresses by the process of forming hypotheses, collecting data, and then testing those hypotheses. Sometimes, the data supports the initial theory, but other times it doesn’t. It's crucial to approach data analysis with a certain level of humility, acknowledging that theories are tentative models of understanding rather than absolute truths. If the data contradicts your hypothesis, it's not a personal failing; it's an opportunity to refine your understanding and develop new theories.

1. A good exercise in analytical thinking is to identify your assumptions. What are you assuming about your data, the relationships between variables, and the statistical methods you are using? Write down your assumptions explicitly so that you can be aware of them and challenge them. If you make any assumptions about your data, be sure to validate them. This may involve using different statistical tests or comparing your results to other data sets. 2. Be wary of data sources that are self-reported. People may tend to over-/under- estimate the positive and negative qualities of their data. 3. Don't be shy to ask other researchers to review your data sources and to provide feedback on their credibility, reliability, and relevance.

I somewhat disagree with the "avoidance of complex data methods". Rather in health data analytics involving millions of medical records, this is necessary for reproducibility, transparency, and accountability. My experience working with big data analytics involved predictive modeling that employed more than one data tool. Let the final data analysis file speak for itself rather than pushing your "biases" during predictive modeling. It includes testing for multicollinearity among model variables. Inform your predictive model through causal diagrams (DAGs) to be as objective as possible in data analysis step. DAG's, and documenting type of predictive modeling utilized helps in accountability, transparency, and reproducibility for largescale.

Start with exploratory data analysis (EDA) to get a better understanding of its characteristics and to identify any potential problems. This can be done using a variety of techniques e.g. visualisation, summarisation, and correlation analysis. Once you have a good understanding of your data, you can choose the right statistical methods to analyse it. It is important to choose methods that are appropriate for your data type, question, and hypothesis. When you're analysing your data, ask yourself questions about what the data is telling you and what it doesn't tell you. Be sceptical of your own conclusions and be willing to revise them based on new evidence.

1. Don't make conclusions based on a single finding or on a small amount of data. Try to replicate your findings and look for other evidence to support your conclusions. 2. Consider the statistical significance of your results before drawing conclusions. Statistical significance is a measure of the probability that your results occurred by chance. 3. Consider the context of your results. What do your results mean in the context of your research question and hypothesis? What are the implications of your results for theory, practice, and policy? 4. Don't ignore negative results. Document and explain them. This shows that you've carefully considered all of your findings and can provide insights even when they don't support your hypothesis.

1. Use data storytelling to communicate your findings to non-technical audiences in a way that is engaging and easy-to-understand. Data storytelling can be enhanced by using appropriate visuals that illustrate the key points of your research. 2. Highlight the key takeaways. Don't overload your audience with too much information. Focus on the most important findings and explain their implications. 3. Be honest and transparent about your findings. Don't try to sugarcoat your results or hide any negative findings. Be honest about the limitations of your study and the implications of your results.