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How can you balance statistical and practical significance in quantitative research?

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Quantitative research is a powerful tool to measure and analyze data, but it also comes with some challenges. One of them is how to balance statistical and practical significance. Statistical significance tells you how likely your results are due to chance, while practical significance tells you how meaningful and relevant your results are for your research question and context. In this article, you will learn what these concepts mean, why they are important, and how you can balance them in your quantitative research.

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Willard Emmanuel Tan

Top Voice | Director | Researcher | Data Analyst | QMS Lead Auditor | Pedagogical Specialist | DACE Certified…

1 What is statistical significance?

Statistical significance is a measure of how confident you can be that your results are not due to random variation or error. It is usually expressed as a p-value, which is the probability of getting your results (or more extreme results) if the null hypothesis is true. The null hypothesis is the assumption that there is no relationship or difference between your variables. A low p-value (typically less than 0.05) means that you can reject the null hypothesis and claim that your results are statistically significant. A high p-value (typically more than 0.05) means that you cannot reject the null hypothesis and that your results are not statistically significant.

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Willard Emmanuel Tan

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Statistical significance is like figuring out if a strange pattern you notice, such as a coin landing on heads more than it should, is just random luck or if there is a real reason behind it. It uses math to see how likely your observation is to happen by chance. If it is very unlikely (usually less than a 5% chance), we say the result is statistically significant, suggesting there is something special causing it. However, it does not tell us how important or big that something is, just that it's probably not just random.

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Francisco Ocazionez

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La significancia estadística ayuda a evaluar si los resultados de una investigación son confiables y no simplemente el resultado del azar. Es crucial considerar el nivel de significancia (generalmente 0,05), interpretar el valor p correctamente y reconocer que la significancia no equivale a importancia práctica. Además, es importante considerar el tama?o de la muestra y la validez de las mediciones para una evaluación completa de la evidencia estadística, ya que cada tipo de estudio o intervención tiene medidas específicas y valores con los que jugar a la hora de realizar las pruebas estadísticas.

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2 What is practical significance?

Practical significance is a measure of how meaningful and relevant your results are for your research question and context. It is not a numerical value, but a qualitative judgment based on your domain knowledge, experience, and goals. Practical significance considers the magnitude, direction, and implications of your results, as well as the costs, benefits, and risks of your actions based on your results. For example, a small increase in sales may be statistically significant, but not practically significant if it does not cover the expenses of your marketing campaign. Conversely, a large decrease in customer satisfaction may be practically significant, but not statistically significant if it is based on a small or biased sample.

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Willard Emmanuel Tan

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Practical significance asks if the observed differences are meaningful in daily life. For example, if one light bulb lasts just 5 minutes longer than another, statistically, this might be significant, but practically, such a small difference might not matter to most people when choosing between the two. It is about the real-world importance of the findings.

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3 Why do you need to balance them?

Statistical and practical significance are not the same thing, and sometimes they may even contradict each other. If you rely only on statistical significance, you may miss the bigger picture and make decisions that are not relevant or useful for your research question and context. If you rely only on practical significance, you may overlook the uncertainty and variability of your data and make decisions that are not supported by evidence or logic. Therefore, you need to balance them and use both criteria to evaluate and interpret your results.

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Willard Emmanuel Tan

Top Voice | Director | Researcher | Data Analyst | QMS Lead Auditor | Pedagogical Specialist | DACE Certified Curriculum Developer | SSG Adult Educator
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Balancing statistical and practical significance is essential to ensure findings are both real and meaningful. Focusing solely on statistical significance might highlight minor differences that do not impact real-world decisions. Similarly, concentrating only on practical significance could lead to overvaluing big differences that might just be due to chance. A balance ensures decisions are based on evidence that is scientifically valid and practically relevant, leading to more informed and effective outcomes.

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4 How can you balance them?

When attempting to balance statistical and practical significance, there is no one-size-fits-all formula or rule. However, several steps can be taken to help. Start by defining your research question and hypothesis clearly, and then choose an appropriate sample size and design that minimize bias and error and maximize power and precision. Additionally, select a suitable level of significance (alpha) and confidence interval (CI) that reflect your desired level of confidence and accuracy. After performing your statistical analysis, interpret the results in relation to your research question and hypothesis, checking for both statistical and practical significance. Consider the context and goals of your analysis when evaluating the results as well. Finally, communicate the results honestly, highlighting both the statistical and practical significance while also noting the limitations and assumptions of the analysis.

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Francisco Ocazionez

Medical Doctor | Data Scientist | Python Developer | Educator | TechHealth | AI in healthcare | Researcher | Guitarist!
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Para lograr un adecuado balance es fundamental considerar: 1. Contextualización: Comprender el contexto y el propósito del estudio para determinar la relevancia práctica. 2. Interpretación precisa: Evaluar tanto el valor p como el tama?o del efecto para determinar la importancia de los hallazgos. 3. Consideración del tama?o de la muestra: Asegurarse de que el tama?o de la muestra sea lo suficientemente grande para detectar diferencias significativas, pero también relevante para el contexto. 4. Aplicación del conocimiento previo: con el fin de contextualizar los resultados y determinar su relevancia práctica. 5. Evaluación de implicaciones prácticas: Analizar estas en el mundo real y cómo pueden influir en la toma de decisiones.

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5 What are some common pitfalls to avoid?

Balancing statistical and practical significance is not easy, and you may encounter some common pitfalls that can compromise the quality and validity of your results. For instance, it is important to not confuse statistical significance with causality, as the former does not imply the latter. Additionally, do not manipulate data or analysis to achieve a low p-value, as this can lead to false positives or overfitting. Moreover, practical significance should not be an afterthought, but a crucial part of your research evaluation and decision making. Furthermore, when interpreting or communicating your results, you should avoid vague terms like "significant" or "insignificant" and instead present them honestly and humbly.

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6 How can you learn more?

Balancing statistical and practical significance is a skill that requires practice and feedback. If you want to improve your quantitative research skills, there are some resources and tips that can help you. Reading books and articles on quantitative research methods and statistics, such as Quantitative Social Research Methods by Kultar Singh or Statistics in Plain English by Timothy C. Urdan, is a great place to start. Additionally, taking online courses or workshops on quantitative research and analysis, such as Quantitative Methods by Coursera or Introduction to Quantitative Research Methods by edX, can be beneficial. Moreover, using software or tools that can help you perform and visualize your analysis, such as SPSS, R, or Excel, can be useful. Seeking advice from experts or peers who can guide you and review your work, such as mentors, supervisors, or colleagues is also important. Finally, getting feedback and criticism from your audience or stakeholders, such as clients, customers, or users can be beneficial.

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7 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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How can you balance statistical and practical significance in quantitative research?

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1 What is statistical significance?

2 What is practical significance?

3 Why do you need to balance them?

4 How can you balance them?

5 What are some common pitfalls to avoid?

6 How can you learn more?

7 Here’s what else to consider

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How can you balance statistical and practical significance in quantitative research?

1

2

3

4

5

6

7

1 What is statistical significance?

2 What is practical significance?

3 Why do you need to balance them?

4 How can you balance them?

5 What are some common pitfalls to avoid?

6 How can you learn more?

7 Here’s what else to consider

Market Research

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