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What are the misconceptions about p-values in research analysis?

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When delving into research analysis, you'll often encounter the term 'p-value.' A p-value, or probability value, is a statistical measure that helps researchers determine the significance of their results. It's a tool used to decide whether the null hypothesis — the default assumption that there is no effect or no difference — can be rejected. However, misconceptions about what p-values represent and how they should be used can lead to misinterpretation of research findings. Understanding these common misunderstandings is crucial for effective research management and accurate analysis.

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Masudi Suleiman

Research Scientist @ Ifakara Health Institute | I develop and apply improved methods to evaluate Vector Control…
Stacy Prokopa

Brand & Growth Strategy Consultant | I help tech product & service companies optimize funnels & build strong…
Mohammad Hossein Khosravi

Clinical Project Manager Medical Devices with +5 Years of Experience | Digital Health & Medtech | AI in Healthcare |…

1 P-value Basics

A p-value is not an indicator of the truthfulness of a hypothesis or the probability that the hypothesis is true. Instead, it measures how compatible your data is with the null hypothesis. A low p-value suggests that your data is less likely to have occurred by random chance under the null hypothesis, but it doesn't prove the alternative hypothesis is true. It's a common mistake to think of p-values as black-and-white evidence when they are actually nuanced indicators of statistical significance.

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Mohammad Hossein Khosravi

Clinical Project Manager Medical Devices with +5 Years of Experience | Digital Health & Medtech | AI in Healthcare | Neuroscientist & Science Communicator | Concussion & Neuromodulation | Clinical Trials & Regulatory
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To avoid over-reliance on p-values as definitive proof of findings, one needs to consider them within the broader context of the experiment, like sample size or effect size, which also influence interpretation. We also need to use p-values alongside other metrics, such as confidence intervals or effect sizes, for a more complete picture of statistical significance.

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2 Misuse in Significance

One of the most prevalent misconceptions is that a p-value below a certain threshold, such as 0.05, confirms a study's findings are significant and important. This threshold is arbitrary and does not guarantee that a result is practically significant or relevant to the real world. You should interpret p-values in the context of the research question, study design, and other evidence rather than viewing them as definitive cut-off points for significance.

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3 Probability Confusion

It's often mistakenly believed that the p-value tells you the probability that the null hypothesis is true or false. However, the p-value is actually the probability of observing data at least as extreme as what was observed, assuming the null hypothesis is true. It does not provide a direct probability about the hypotheses themselves, which is a subtle but crucial distinction that can change how you interpret your research findings.

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Stacy Prokopa

Brand & Growth Strategy Consultant | I help tech product & service companies optimize funnels & build strong positioning | US, UK, CEE, SEA
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A misunderstanding about p-values is that they are the chances are that the null hypothesis is correct. In fact, it is a measure of the likelihood of getting the measured results or even results more outrageous if there is actually no difference at all between the groups in question. To avoid confusion: - Don't interpret p-values as a measure of truth. - Use them as one part of your analysis, not the sole decision-maker. There are many occasions when I watched how teams make quick decisions based on low p-values and without thinking that p-values should be considered along with other parameters.

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4 Certainty Misinterpretation

Another common error is equating a low p-value with certainty about a result. A low p-value does not mean that there is absolute certainty regarding the findings. All statistical tests have a degree of error, and a p-value simply quantifies the evidence against the null hypothesis within the framework of the test. It's essential to remember that statistical significance does not equate to certainty or to the practical importance of a result.

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5 Binary Thinking

Researchers sometimes fall into binary thinking, where they consider p-values as definitive proof that an effect exists or doesn't exist. In reality, p-values provide a gradient measure of evidence against the null hypothesis. Treating them as 'yes-or-no' indicators can lead to oversimplified conclusions and neglects the probabilistic nature of statistical analysis. Recognize that p-values contribute to a broader understanding rather than providing a simple answer.

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6 Contextual Importance

Finally, the importance of context in interpreting p-values cannot be overstated. P-values should be considered alongside the study design, sample size, effect sizes, and other relevant factors. Ignoring the context can lead to overestimating the importance of p-values and making inappropriate conclusions about the data. Always integrate p-values into a comprehensive analysis of all relevant research factors for accurate interpretation.

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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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Masudi Suleiman

Research Scientist @ Ifakara Health Institute | I develop and apply improved methods to evaluate Vector Control Products | Passionate about climate change and its impact on mosquito vectors and associated diseases
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One may also confuse that a p-value shows the probability of replicating study findings, reproducibility depends on many factors such as study design, sample size and characteristics and the true underlying effect.

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What are the misconceptions about p-values in research analysis?

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1 P-value Basics

2 Misuse in Significance

3 Probability Confusion

4 Certainty Misinterpretation

5 Binary Thinking

6 Contextual Importance

7 Here’s what else to consider

Research Management

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