How can you interpret p-values in the context of your research?

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Understanding p-values is a fundamental aspect of data analytics, particularly when you're trying to make sense of the results from your research. In essence, a p-value helps you determine the strength of your findings by calculating the probability that your results could have occurred by chance. This number can guide you in deciding whether to reject your null hypothesis, which is the default assumption that there is no effect or no difference in your experiment. A low p-value indicates that your findings are statistically significant, meaning that what you observed in your data is unlikely to be due to random chance and thus worthy of further consideration.

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Bruno Nwagbo

Data Scientist || Data Analyst

1 P-Value Basics

To properly interpret p-values, you must first understand what they represent. A p-value is a statistical measure that helps to determine the significance of your research results. When you perform a hypothesis test, the p-value helps you understand the likelihood that the results you are observing are due to chance. If this value is low, typically less than 0.05, it suggests that your results are statistically significant and not likely due to random variation. This threshold, however, is arbitrary and should be considered in the context of your specific research question and field of study.

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Bruno Nwagbo

Data Scientist || Data Analyst
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I agree with the article. I will try to explain better. The "p" in p-value stands for "probability." The p-value represents the probability of obtaining an observed result, or one more extreme, assuming that the null hypothesis is true. It is a measure used in statistical hypothesis testing to assess the strength of the evidence against the null hypothesis. Let me explain null hypothesis. Null hypothesis asserts no effect or relationship exists between two test sets/ occurrences. For example, you are working on a new drug that reduces blood pressure. You hypothesised that the new drug lowers blood pressure. Your null hypothesis will be the new drug has no effect on blood pressure. P-value helps you determine this.

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2 Significance Levels

The significance level, often denoted as alpha (α), is a threshold against which the p-value is compared to assess the strength of the evidence against the null hypothesis. Commonly set at 0.05, this level indicates a 5% risk of concluding that a difference exists when there is no actual difference. If your p-value is less than α, you can reject the null hypothesis with confidence. However, it's crucial to choose an appropriate significance level for your study, as a lower alpha reduces the risk of false positives but increases the risk of false negatives.

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Bruno Nwagbo

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Very true. Let me add my perspective. Before I begin, the researcher can choose their significance level based on their criteria. It isn’t a set rule to use 0.05. I worked on a regression model and was tasked with selecting the best features for my model. I used a significance level of 0.001 to select the significantly stronger features among others. Yes, many people use 0.05, but it isn’t a set rule. Assuming the significance level (SL) was set at 0.05. A p-value less than SL means your observed effect has a high probability and was not by chance, thereby rejecting the null hypothesis. A p-value greater than SL means that occurrence was by luck, and you must accept your null hypothesis.

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3 Context Matters

The interpretation of a p-value also depends on the context of your research. It's not just about whether the p-value is lower than the significance level but also about the practical significance of your findings. For example, a very low p-value in a large study might indicate a statistically significant result that is not practically meaningful. Conversely, a higher p-value in a small study might still be relevant in a real-world context. Therefore, always consider the size and scope of your study, as well as the practical implications of your results.

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Bruno Nwagbo

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Yes, this is true. This is where domain knowledge, scope, or experience comes into play. In machine learning, I use the p-value to check the significance of various features, but it doesn’t stop there. To balance your research, or in my case, my model, I have to use domain knowledge. There should be a rationale behind the p-value you obtain, considering not only its statistical significance but also its practical relevance. For instance, a feature with a very low p-value might still be unimportant if it does not contribute meaningfully to the model's performance or if it lacks practical significance in the given context.

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4 Misconceptions Cleared

A common misconception is that the p-value can tell you the probability that the null hypothesis is true or false. However, it only informs you about the data's compatibility with the null hypothesis. A small p-value does not prove that there is a true effect, just as a large p-value does not prove there isn't one. It is simply a tool to gauge the evidence against the null hypothesis provided by your data. Remember, statistical significance does not equate to practical significance, and other factors such as effect size should be considered.

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Bruno Nwagbo

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Absolutely, and it's crucial to integrate the p-value with other statistical measures such as confidence intervals and effect sizes to get a more comprehensive understanding of the data. Additionally, consider the context and practical implications of your findings. For instance, a statistically significant result with a small effect size might not be practically meaningful, while a non-significant result in a small sample might still warrant further investigation. Remember, the scope of your research objectives and real-world relevance should be a major determinant. I always interpret your p-value within the boundaries of your research scope

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5 Beyond P-Values

While p-values are a staple in statistical analysis, they are not the be-all and end-all. Other statistical measures, such as confidence intervals and effect sizes, provide additional context to your findings. Confidence intervals give a range within which the true effect size is likely to lie, offering more information than a single p-value can. Effect sizes quantify the magnitude of the difference or relationship, helping you understand the practical importance of your results.

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6 Continuous Learning

As you delve deeper into data analytics, you'll find that interpreting p-values is just one aspect of a broader statistical understanding. It's important to stay abreast of best practices and evolving methodologies. Engage with the analytics community and seek out educational resources to refine your approach to data interpretation. With experience and ongoing learning, you'll become more adept at making informed decisions based on the statistical evidence provided by p-values and other analytical tools.

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Bruno Nwagbo

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Do not rush. With time, you will understand and learn to see the big picture. Community is key but find a very good community

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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 interpret p-values in the context of your research?

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1 P-Value Basics

2 Significance Levels

3 Context Matters

4 Misconceptions Cleared

5 Beyond P-Values

6 Continuous Learning

7 Here’s what else to consider

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