What are the best ways to ensure unbiased statistical analysis?

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Statistical analysis is a powerful tool for finding patterns, testing hypotheses, and making decisions based on data. However, it also requires careful attention to avoid bias, which can distort the results and lead to false or misleading conclusions. Bias can arise from various sources, such as the design of the study, the selection of the data, the choice of the methods, and the interpretation of the findings. In this article, you will learn some of the best ways to ensure unbiased statistical analysis in the context of operations research (OR), a discipline that applies analytical techniques to solve complex problems in various fields.

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Rafael Matos, MSc.

Lead Data Scientist at Neon | Credit Risk Modeling | IFRS 9 | Loan Loss Provisions | Quantitative Finance | Retail…

1 Define the problem and the objective

Before you start collecting and analyzing data, you need to have a clear and specific definition of the problem you want to solve and the objective you want to achieve. This will help you to avoid framing bias, which is the tendency to view the problem from a narrow or subjective perspective. You should also consider the ethical and social implications of your problem and objective, and how they align with the values and goals of your stakeholders. By defining the problem and the objective in a comprehensive and transparent way, you will set the foundation for unbiased statistical analysis.

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Hafeez Ur Rehman

Application Specialist | Spectral data analyst | Science Communicator | Microplastics
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To ensure unbiased statistical analysis, use randomized data selection, and blind analysis methods, employ cross-validation techniques, and maintain transparency in data handling and methodology.

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Brian Muyambo

Research Consultancy Services | Market Research Specialist | Data Analytics | Training services
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Start by precisely defining the research question and outlining the objectives you aim to explore. This ensures you stay focused and avoid tailoring the analysis to confirm pre-existing beliefs. Example: Instead of framing the question as "Does this drug improve patient outcomes?" (which could lead to focusing on positive results), ask "What is the effect of this drug on patient outcomes compared to a placebo?"

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2 Choose a suitable design and sample

The next step is to choose a suitable design and sample for your study, which will determine how you collect and measure the data. You should aim for a design that minimizes confounding factors, which are variables that affect both the outcome and the predictor of interest. For example, you can use randomization, stratification, or matching to reduce confounding. You should also aim for a sample that represents the population of interest, and avoid selection bias, which is the tendency to exclude or include certain groups or individuals based on non-random criteria. You should also consider the sample size, which affects the power and precision of your analysis.

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Brian Muyambo

Research Consultancy Services | Market Research Specialist | Data Analytics | Training services
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Employ random sampling techniques to ensure every individual or data point in your population has an equal chance of being selected. This prevents skewing the results towards a specific subgroup. Consider implementing blind analysis, where the individuals analyzing the data are unaware of the groups they represent (e.g., treatment vs. control group). This minimizes the influence of unconscious biases on interpretation. Example: When testing a new educational program, randomly select students from different classes to participate, and have an independent researcher analyze the results without knowing which students belonged to the program group.

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3 Apply appropriate methods and techniques

Once you have the data, you need to apply appropriate methods and techniques to analyze it and test your hypotheses. You should choose methods and techniques that match the type and distribution of your data, and avoid data dredging, which is the practice of searching for significant results without a priori hypotheses or justification. You should also avoid confirmation bias, which is the tendency to favor or interpret data that supports your preconceptions or expectations. You should also use robust and reliable methods and techniques, and check for errors, outliers, and assumptions.

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4 Report and communicate the results

The final step is to report and communicate the results of your analysis, and draw conclusions and recommendations based on them. You should report and communicate the results in an honest and accurate way, and avoid reporting bias, which is the tendency to omit or emphasize certain aspects of the results based on their significance or direction. You should also provide sufficient detail and transparency about your data, methods, techniques, and limitations, and use appropriate visualizations and summaries to convey the main findings. You should also acknowledge the uncertainty and variability of your results, and use confidence intervals, p-values, or other measures of significance and effect size.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rafael Matos, MSc.

Lead Data Scientist at Neon | Credit Risk Modeling | IFRS 9 | Loan Loss Provisions | Quantitative Finance | Retail Banking | Machine Learning | Data Science | Complex Systems MSc.
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As I commented in a similar article, It’s impossible to eliminate all possible bias in modeling. But there are some strategies such as (non-exhaustive list): - Assure that sampling methodology is fair; - Avoid data leakage; - When using optimizers, use random starting guesses for hyperparameters; - Avoid model overfitting, since overfitting is itself a training bias; - Use validation methods that are actually appropriate for the data/model combination (e.g. cross validation works for iid data. Is your data iid?) - Watch for other aspects such as endogeneity.

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What are the best ways to ensure unbiased statistical analysis?

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1 Define the problem and the objective

2 Choose a suitable design and sample

3 Apply appropriate methods and techniques

4 Report and communicate the results

5 Here’s what else to consider

Operations Research

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What are the best ways to ensure unbiased statistical analysis?

1

2

3

4

5

1 Define the problem and the objective

2 Choose a suitable design and sample

3 Apply appropriate methods and techniques

4 Report and communicate the results

5 Here’s what else to consider

Operations Research

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