How can you avoid misusing p-values when conducting statistical analysis?

To avoid misusing p-values: 1# Understand p-values: They indicate the probability of the observed data under the null hypothesis. 2# Avoid strict thresholds: Don’t use p-values as rigid cut-offs (e.g., p < 0.05). 3# Report additional metrics: Include effect sizes and confidence intervals. 4# Avoid p-hacking: Don’t manipulate analyses to achieve significance. 5# Pre-register studies 6# Adjust for multiple testing 7# Recognize limitation: Understand p-values don't measure practical significance or hypothesis confirmation.

It's critical to have an a-priori hypothesis and to run a power analysis to understand how much data is necessary to see the anticipated result. There's always significance out there and if you go in blind, you'll likely find it, but it's not always as meaningful as the hopeful scientist would like to make it out to be.

Three Important things to consider when Talking about P values: 1.Is your data calculated with parametric or non-parametric test? Means if your data is normally distributed or not? 2. is there a chance of Type I or Type II error? How can you validate that? 3. Choosing right power analysis with your pilot experiment. Other than this, there could be some Outliers which can impact the overall P values. These three check points will get rid of P value misusage.

Para evitar el mal uso de los valores p, es crucial comprender que representan la probabilidad de obtener un resultado extremo si la hipótesis nula es verdadera, y no son una prueba de verdad. No dependas únicamente del valor p; considera el contexto, tama?os de efecto, intervalos de confianza y relevancia práctica. Evita el p-hacking planificando y pre-registrando tus análisis, y reporta todos los resultados, significativos o no. Interpreta correctamente los resultados, entendiendo que significancia no implica importancia y evitando umbrales arbitrarios. Utiliza intervalos de confianza para proporcionar estimaciones precisas y mantén una formación continua en estadística para aplicar correctamente estas métricas.

Set the threshold value, which signifies your level of type one error you are willing to commit with your analysis. A low threshold value implies a high level of confidence in the conclusions drown out of your analysis.

Defina os limites a priori, n?o faz sentido explorar a "probabilidade" depois, pode deixar a análise subjetiva. Assim como os níveis de significancia também podem depender do contexto e da precis?o do modelo em quest?o.

To avoid misusing p-values in statistical analysis, set a clear significance threshold (commonly α = 0.05) before collecting data. This threshold defines the cutoff for determining statistical significance, reducing the risk of arbitrary or post-hoc decisions. Understand that a p-value below this threshold indicates evidence against the null hypothesis, but it doesn't measure the size or importance of an effect. Always report p-values in conjunction with effect sizes and confidence intervals to provide a more comprehensive understanding of the results. Finally, ensure reproducibility by adhering to a predefined analysis plan and transparency in reporting methods and findings.

Arbitrary thresholds like p<0.05 oversimplify complex research realities. Context matters - some fields need stricter cutoffs, others more lenient. Pre-registering your threshold prevents post-hoc manipulation. Better yet, report exact p-values and focus on practical significance. Let's move beyond binary "significant/not significant" thinking for more nuanced, meaningful research.

Setting the right significance level isn’t just about sticking to the usual p-value of 0.05. Through my projects, I’ve realized that this number doesn’t fit every situation—especially when the outcomes really matter, like in health studies or financial analyses. Choosing this threshold thoughtfully, based on what your data actually represents and the potential impacts of your findings, is crucial. Remember, tweaking this number after peeking at the results can lead us down the wrong path. It’s all about integrity in our approach to ensure our conclusions are sound.

Sometimes we have to get back to the basics of statistics and remind ourselves that p values are highly dependent on sample size. Let’s look at this formula for example: t = (sample mean - null hypothesis) value / (sample standard deviation/sqrt(sample size) If your study is underpowered (i.e., small N), it can lead to misinterpretation of results and incorrect conclusions. A large N, however, can detect statistically significant differences but it doesn’t mean that they’re relevant. Also, we have to be critical of the data we have and research methods used to interpret our findings. Exercise caution and do your due diligence on understanding the variability in your population, and what you can realistically do with your data.

Keep in mind the bigger picture! Don't ignore the following contexts- 1.Population: Sample (Size and qualities) 2. How critical are the decisions which will be made based on the p-value (eg. medical decisions) 3. Effect size is imp, p-value sometimes could be deceptive. 4. Available Literature- whether it supports the findings.

Remember that a p-value is not the sole indicator of scientific truth but a tool within a broader framework of evidence. Always interpret p-values alongside other statistical measures like effect size, confidence intervals, and the study's practical significance. Understand the underlying assumptions and limitations of the tests used. Remember, a statistically significant result might not be practically significant. Lastly, place findings within the context of existing literature and theoretical frameworks to ensure a well-rounded and accurate interpretation of your data.

La valeur p doit être interprétée dans le contexte de l’étude. Une valeur p de 0,04 peut être significative dans une étude exploratoire sur les habitudes alimentaires, mais insuffisante dans une étude confirmatoire sur la sécurité d'un médicament. Par ailleurs, la taille de l’effet et l'importance pratique des résultats doivent être considérées en parallèle. Par exemple, une petite différence statistiquement significative dans une grande étude peut être moins pertinente qu'une grande différence non significative dans une petite étude.

Common P-Hacking examples: 1-Multiple Comparisons: Conducting a large number of statistical tests without adjusting since it increases the chance of finding at least one statistically significant result purely by chance. 2-Selective Reporting: Only reporting experiments or analyses that yielded significant results, while ignoring the others. 3-Data Exclusion: Arbitrarily excluding data points or subjects to achieve significant results. 4-Stopping Data Collection Early: Stopping data collection once significant results are obtained, rather than following a pre-determined sample size or study duration. 5-Changing Hypotheses: Formulating or altering hypotheses after seeing the results to make the findings appear significant.

To avoid misusing p-values, first clearly define your null hypothesis, which states there is no effect or difference between the groups you're studying. For example, if you're testing a new drug, your null hypothesis might be that the drug has no effect on patients compared to a placebo. Misinterpretation happens if the null hypothesis is vague or misunderstood. Make sure it’s specific and testable. Also, avoid p-hacking, which means manipulating data to get significant results. This can include reporting only favorable outcomes or stopping data collection early. To prevent this, pre-register your study details and stick to your plan. This ensures your p-value is trustworthy and unbiased.

In my journey through data analysis, I’ve seen how tempting it can be to tweak our methods until we get the results we want—a practice known as p-hacking. But real insights come from integrity in our methods, not just the results that please us. Pre-registering our study design, sticking to our initial hypotheses, and rigorously following our analysis plan are crucial. These steps aren’t just about avoiding p-hacking; they’re about ensuring our findings are truly valuable and not just statistical artifacts. Remember, good science is replicable and transparent, not just significant.

To avoid misusing p-values through p-hacking, adhere to a pre-registered analysis plan that outlines your hypotheses, methods, and statistical tests before collecting data. Avoid selectively reporting only significant results or manipulating data to achieve desirable p-values. Conduct and report all analyses transparently, including non-significant findings, to maintain integrity. Use proper corrections for multiple comparisons when conducting several tests. Emphasize the importance of replication and validation of findings through independent studies. By maintaining rigorous and honest research practices, you ensure the validity and reliability of your statistical conclusions.

Cela inclut aussi des pratiques telles que tester plusieurs hypothèses sans correction, regrouper ou subdiviser les données de différentes manières, ou arrêter la collecte de données dès qu’une valeur p significative est obtenue. Pour éviter cela, il est essentiel de préenregistrer les protocoles d’étude et d’adopter des analyses planifiées a priori. Par exemple, dans une étude sur les effets d'un programme d'exercice, décider à l'avance quelles mesures seront analysées et comment.

To avoid misusing p-values, ensure the replicability of your findings. A single study's p-value can be misleading due to random variability or sampling error. Replication, by conducting the same study under different conditions or with different samples, helps verify the robustness and reliability of the results. Consistent findings across multiple studies strengthen the evidence against the null hypothesis and provide a clearer picture of the true effect. Emphasize the importance of replicability in your research design and reporting, fostering a more accurate and trustworthy scientific process.

Un seul résultat statistiquement significatif n’est pas suffisant pour établir une vérité scientifique. La réplication des études, idéalement par des chercheurs indépendants, est nécessaire pour confirmer les résultats et réduire les risques de faux positifs. Par exemple, des études initiales sur les effets bénéfiques du café sur la santé doivent être reproduites par d'autres chercheurs pour confirmer ces résultats avant de tirer des conclusions définitives.

John Iodannis (2005) claimed that much of modern research is not rigorous because it cannot be reproduced. Subsequent meta-analysis approximated that only 36% of published research of the time could be replicated. This probably speaks most to the nature of humans to subtly change their perspective and environment to seek their personally desired outcomes. When evaluating the quality of research (and what this implies about the trustworthiness of the claimed relationships/outcomes) it is useful to also review similar work from independent organisations or researchers.

Replication isn’t just a box to tick; it’s the backbone of credible research. During my experience in data analysis, I’ve learned that a single study is only a starting point. Repeating the study in different settings, with diverse samples, not only confirms the findings but also uncovers new insights. Each time we replicate a study, we strengthen our confidence in the results, ensuring that our conclusions are robust enough to withstand scrutiny and time

Other factors worth considering include effect size, confidence intervals, and the study's overall context. P-values can be influenced by sample size. Eg, a large enough sample, even minor effects can result in low p-values. Hence, relying solely on p-values without considering the practical implications of the findings can be deceptive.

Along with the p values other metrics such as confidence intervals or effect sizes can be provided. The most important thing is to let the data tell the real story and not vice versa

Report Exact p-values: Instead of simply stating that p < 0.05, report the exact p-value. This provides more information about the strength of the evidence against the null hypothesis. Pre-register Your Study: Pre-registration involves detailing your study design, hypotheses, and analysis plan before collecting data. This reduces the risk of p-hacking (manipulating data or analyses to achieve significant results). Beware of Multiple Comparisons: When conducting multiple statistical tests, the chance of finding at least one statistically significant result due to random chance increases. Use corrections for multiple comparisons (e.g., Bonferroni correction) to adjust p-values accordingly.

En plus des valeurs p, il est recommandé de rapporter d’autres métriques comme les intervalles de confiance, les tailles d’effet et les valeurs Bayésiennes. Par exemple, un intervalle de confiance pour la différence de moyenne entre deux groupes peut fournir des informations sur la précision de l'estimation. Les tailles d'effet, comme la taille de Cohen, indiquent l'ampleur de l'effet observé, ce qui est crucial pour évaluer l'importance pratique des résultats. Les approches Bayésiennes peuvent fournir des probabilités plus intuitives sur l’hypothèse testée.

What is the study size? Then critical thinking - CI, p values that appear to show significance - interpret with caution as statistical significance may not necessarily equate to clinical significance.

I felt a lack of encouragement to read and share information; in the scientific world, exchange should be encouraged (instead of being condemned).