{"id":1143,"date":"2025-11-10T17:57:40","date_gmt":"2025-11-10T20:57:40","guid":{"rendered":"https:\/\/www.kachi.com.br\/?p=1143"},"modified":"2025-11-10T18:05:33","modified_gmt":"2025-11-10T21:05:33","slug":"evidence-or-coincidence-insights","status":"publish","type":"post","link":"https:\/\/www.kachi.com.br\/en\/evidence-or-coincidence-insights\/","title":{"rendered":"Evidence or Coincidence? Insights Into Statistics and the Scientific Method"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">We are constantly asking questions like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Is this vaccine effective in preventing the flu?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Is this medication effective in treating COVID-19?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Does this substance cause cancer?<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Clinical practice is grounded in scientific data and research findings. For medicine to evolve, the scientific community continuously asks relevant questions and conducts studies to answer them.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/UdYu\"><span style=\"font-weight: 400;\"><sup>1<\/sup><\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Every study begins with a question! Researchers then formulate a hypothesis to be tested\u2014for example, whether a specific treatment is more effective than existing ones. From there, they establish objectives, define the characteristics of participants (study population), determine how outcomes will be evaluated (e.g., which tests or assessments will be conducted), and finally use statistics to <\/span><i><span style=\"font-weight: 400;\">assess <\/span><\/i><span style=\"font-weight: 400;\">the probability that the hypothesis reflects a true effect.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/UdYu\"><span style=\"font-weight: 400;\"><sup>1<\/sup><\/span><\/a><span style=\"font-weight: 400;\">,<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/UouY\"><span style=\"font-weight: 400;\"><sup>2<\/sup><\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">This article will explore some statistical concepts and scientific methods used to answer questions, distinguishing real data from mere coincidences.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b>Correlation and Causation<\/b><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">The term &#8220;correlation&#8221; is often used in everyday language to describe some form of association, such as the arrival of cold weather and the increase in respiratory issues.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Statistically, correlation refers to an association between two quantitative variables (characteristics that can be measured, such as weight and height). It also assumes a linear association, meaning one variable increases or decreases by a fixed value as the other increases or decreases (Figure 1).<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/X7ut\"><span style=\"font-weight: 400;\"><sup>3<\/sup><\/span><\/a><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-1144 size-full\" src=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_1.png\" alt=\"\" width=\"839\" height=\"446\" srcset=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_1.png 839w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_1-300x159.png 300w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_1-768x408.png 768w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_1-820x436.png 820w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_1-600x319.png 600w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_1-200x106.png 200w\" sizes=\"auto, (max-width: 839px) 100vw, 839px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Figure 1. Illustration showing a linear association (A) and uncorrelated data (B). Adapted from The BMJ. <\/span><a href=\"http:\/\/paperpile.com\/b\/cZNHHk\/X7ut\"><span style=\"font-weight: 400;\">Correlation and regression<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><span style=\"font-weight: 400;\"><sup>3<\/sup><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b>Correlation only measures association; it cannot determine cause and effect.<\/b><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/Fc6c\"><b><sup>4<\/sup><\/b><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Causality, on the other hand, remains a topic of ongoing debate in the scientific community. Simply put, the cause of an event can be defined as a condition or characteristic that existed before the event and was necessary for it to occur. In other words, to infer causation, the condition must precede the outcome, and the outcome must not occur without that condition.<\/span><span style=\"font-weight: 400;\"><sup>5<\/sup><\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">It&#8217;s possible to find associations that lack causality. Some associations may occur purely by chance\u2014these are false associations.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/nlzz\"><span style=\"font-weight: 400;\"><sup>6<\/sup><\/span><\/a><span style=\"font-weight: 400;\">,<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/9cJ5\"><span style=\"font-weight: 400;\"><sup>7<\/sup><\/span><\/a><span style=\"font-weight: 400;\"> Below is a humorous example to illustrate this point:<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b>Nicolas Cage and U.S. Drowning Cases<\/b><\/p>\n<p><span style=\"font-weight: 400;\">When comparing random data sets, it&#8217;s possible to find high associations purely by chance.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/9cJ5\"><span style=\"font-weight: 400;\"><sup>7<\/sup><\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A fascinating example emerged a few years ago: a correlation between &#8220;the number of people who drowned in swimming pools in the U.S.&#8221; and &#8220;the number of films starring Nicolas Cage released from 1999 to 2009&#8221; (Figure 2). Interestingly, drowning cases seemed to follow the number of Nicolas Cage movies.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/9cJ5\"><span style=\"font-weight: 400;\"><sup>7<\/sup><\/span><\/a><span style=\"font-weight: 400;\"> However, despite the correlation, we cannot conclude that the actor is responsible for the drownings!<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-1146 size-full\" src=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_2.png\" alt=\"\" width=\"839\" height=\"409\" srcset=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_2.png 839w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_2-300x146.png 300w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_2-768x374.png 768w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_2-820x400.png 820w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_2-600x292.png 600w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_2-200x97.png 200w\" sizes=\"auto, (max-width: 839px) 100vw, 839px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Figure 2. Correlation between the number of drownings in the US and Nicolas Cage&#8217;s movies. Adapted from <\/span><span style=\"font-weight: 400;\">Vigen T. Spurious correlations.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/TMa7\"><span style=\"font-weight: 400;\"><sup>8<\/sup><\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">These coincidences can also occur due to unaccounted-for variables that explain the observed association. Such variables are called confounding or &#8220;hidden&#8221; variables.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/9cJ5\"><span style=\"font-weight: 400;\"><sup>7<\/sup><\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">For instance, suppose a study finds that people who drink a lot of coffee have a lower risk of skin cancer. This doesn&#8217;t necessarily mean coffee has protective properties against cancer! An alternative explanation might be that heavy coffee drinkers spend long hours indoors and, therefore, have less sun exposure\u2014a known risk factor for skin cancer. In this case, reduced outdoor exposure is a confounding variable, common to both coffee consumption and skin cancer risk (Figure 3).<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/nlzz\"><span style=\"font-weight: 400;\"><sup>6<\/sup><\/span><\/a><\/p>\n<p><b>In short, observing an association suggests a hypothesis but does not provide evidence that one variable causes the other.<\/b><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/nlzz\"><b><sup>6<\/sup><\/b><\/a><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-1148 size-full\" src=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3.png\" alt=\"\" width=\"839\" height=\"863\" srcset=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3.png 839w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3-292x300.png 292w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3-768x790.png 768w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3-820x843.png 820w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3-797x820.png 797w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3-583x600.png 583w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_3-194x200.png 194w\" sizes=\"auto, (max-width: 839px) 100vw, 839px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Figure 3. Examples of associations between data and true\/false causalities. Adapted from Altman N e Krzywinski M. Nat Methods 2015;12(10):899\u2013900.<\/span><span style=\"font-weight: 400;\">6<\/span><span style=\"font-weight: 400;\"> Chang M. Educ Psychol Meas 2017;77(3):475\u201388.<\/span><span style=\"font-weight: 400;\"><sup>9<\/sup><\/span><span style=\"font-weight: 400;\">).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To infer that one variable causes another, there are scientific methods and statistical tests necessary to do so.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/Fc6c\"><span style=\"font-weight: 400;\"><sup>4<\/sup><\/span><\/a><\/p>\n<p><b>Conditions for Causality<\/b><\/p>\n<p><span style=\"font-weight: 400;\">All events result from previous events, which were caused by other events, and so on.<\/span> <span style=\"font-weight: 400;\">Causal relationships between events require three conditions:<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/97sz\"><span style=\"font-weight: 400;\"><sup>9<\/sup><\/span><\/a><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The &#8220;cause event&#8221; precedes the &#8220;effect event&#8221; in time.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The factor isolation law is true: if X, then Y; if not X, then not Y.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The relationship is verifiable, meaning condition 2 persists over time and the events in condition 1 are repeatable. Repetition allows causality to inform predictions.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">For example, we know that SARS-CoV-2 is the causative agent of COVID-19.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/gsZs\"><span style=\"font-weight: 400;\"><sup>10<\/sup><\/span><\/a><span style=\"font-weight: 400;\"> So:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Viral infection precedes the disease (COVID-19) in time.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">If a person is infected (and susceptible), they will develop the disease; if not infected, they won&#8217;t get COVID-19.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Numerous individuals infected with SARS-CoV-2 developed the disease.<\/span><\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<p><b>Statistical Significance<\/b><\/p>\n<p><span style=\"font-weight: 400;\">When researchers face a scientific question, they use existing data to hypothesize and test it statistically.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Statistical significance assesses whether study findings are likely due to chance or represent real patterns in the data.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/UouY\"><span style=\"font-weight: 400;\"><sup>2<\/sup><\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">When results are statistically significant, the null hypothesis (e.g., no difference between two groups) is rejected, suggesting an actual difference. For instance, if experimental and control group responses are identical, we cannot reject the null hypothesis. Thus, within the study&#8217;s criteria, there seems to be no difference.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/WZSB\"><span style=\"font-weight: 400;\"><sup>11<\/sup><\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><b>The P-Value<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Most statistical tests conclude with a P-value calculation.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/bBzr\"><span style=\"font-weight: 400;\"><sup>12<\/sup><br \/>\n<\/span><\/a><b>,<\/b><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/LTZF\"><span style=\"font-weight: 400;\"><sup>13<\/sup><\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">The P-value represents the probability of observing an effect as extreme as the one found, assuming the null hypothesis is true. Lower P-values indicate results are less consistent with the null hypothesis.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/bBzr\"><span style=\"font-weight: 400;\"><sup>12<\/sup><\/span><\/a><\/p>\n<p><i><span style=\"font-weight: 400;\">But how can we understand this in practice?<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">Imagine a randomized clinical trial that compares a new antidepressant drug with a placebo. At the end of the study, it was shown that 60% of patients treated with the new antidepressant and 40% of those treated with placebo had a good response; the calculated P-value is 0.03.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/LTZF\"><span style=\"font-weight: 400;\"><sup>13<\/sup><\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">What, then, is the appropriate interpretation of this result? Imagine that the null hypothesis is true; that is, the new antidepressant is no different from placebo. Now, if you were to conduct a hundred randomized controlled trials comparing the drug to placebo, you would certainly not get an identical response rate for the drug and placebo in each RCT. Instead, in some RCTs, the drug would outperform the placebo, and in others, the placebo would outperform the drug. Furthermore, the magnitude by which the drug and placebo outperformed each other would vary from trial to trial.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/LTZF\"><span style=\"font-weight: 400;\"><sup>13<\/sup><\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this context, what P = 0.03 (i.e., 3%) means is that if the null hypothesis is true, and if you ran the study a large number of times in exactly the same way, then on 3% of occasions you would get the same or a larger difference between the groups than you got on this occasion.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/LTZF\"><span style=\"font-weight: 400;\"><sup>13<\/sup><\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">However, you need a criterion for defining statistical significance! \ud835\udec2 is that criterion. It is a probability that we accept of considering H0 false when, in fact, it is true. In other words, it is the maximum error that we are willing to accept. It is set arbitrarily. For example, \u03b1 = 5%. If \ud835\udec2 = 0.05 and P = 0.03 (i.e., P is less than \ud835\udec2 and further from H0), then statistical significance is reached. If \ud835\udec2 = 0.01 and P = 0.03, statistical significance is not reached. Intuitively, if the P value is less than the pre-specified \ud835\udec2, then the data suggest that the study result is so rare that it does not appear to be consistent with H0, leading to the rejection of H0. For example, if the P value is 0.001, this indicates that, if the null hypothesis is indeed true, there would be only a 1 in 1,000 chance of observing data at this extreme. Therefore, either very unusual data were observed or the assumption about the truth of H0 is incorrect. Thus, small P values \u200b\u200b(smaller than \ud835\udec2) lead to rejection of H0 in favor of an H1 of some effect (e.g., effect of some treatment).<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/WZSB\"><span style=\"font-weight: 400;\"><sup>11<\/sup><\/span><\/a><span style=\"font-weight: 400;\">,<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/bBzr\"><span style=\"font-weight: 400;\"><sup>12<\/sup><\/span><\/a><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-1150 size-full\" src=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_4.png\" alt=\"\" width=\"839\" height=\"218\" srcset=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_4.png 839w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_4-300x78.png 300w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_4-768x200.png 768w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_4-820x213.png 820w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_4-600x156.png 600w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_4-200x52.png 200w\" sizes=\"auto, (max-width: 839px) 100vw, 839px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p><b>Why 5%?<\/b><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">Virtually all healthcare professionals are familiar with the expression \u201cP&lt;0.05\u201d as a cutoff that indicates \u201cstatistical significance.\u201d<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/LTZF\"><span style=\"font-weight: 400;\"><sup>13<\/sup><\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">For decades, 0.05 (5%, or 1 chance in 20) was conventionally accepted as the threshold for discriminating significant from insignificant results, inadequately translated into differences or phenomena that exist from those that do not. In practice, in a normal distribution curve, while 95% of the area under the curve falls between +2 and -2 standard deviations from the center, 5% would be at the tails of the curve (Figure 4).14 This means that approximately 5% of the normal distribution comprises outlying or \u201csignificantly different\u201d values, that is, values \u200b\u200bthat are more than two standard deviations away from the mean.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/WZSB\"><span style=\"font-weight: 400;\"><sup>11<\/sup><\/span><\/a><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Thus, a P value of less than 0.05 (i.e., 5%) means that if the null hypothesis (H0) is true, and if you perform the study a large number of times in exactly the same way, then on 5% of the occasions you would obtain an equal or greater difference between the groups than you obtained on this occasion. This is something so rare that we can consider that H0 has a high chance of being incorrect!<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/LTZF\"><span style=\"font-weight: 400;\"><sup>13<\/sup><\/span><\/a><\/p>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-1152 \" src=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_5.png\" alt=\"\" width=\"830\" height=\"474\" srcset=\"https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_5.png 830w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_5-300x171.png 300w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_5-768x439.png 768w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_5-820x468.png 820w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_5-600x343.png 600w, https:\/\/www.kachi.com.br\/wp-content\/uploads\/2025\/11\/evidence-or-coincidence_artes-_5-200x114.png 200w\" sizes=\"auto, (max-width: 830px) 100vw, 830px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Figure 4. Example of a normal distribution curve. Adapted from <\/span><a href=\"http:\/\/paperpile.com\/b\/cZNHHk\/ArCm\"><span style=\"font-weight: 400;\">Di Leo G et al. Eur Radiol Exp 2020;4(1):18.<\/span><\/a><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/ArCm\"><span style=\"font-weight: 400;\"><sup>14<\/sup><\/span><\/a><\/p>\n<p><b>Conclusions<\/b><b><\/b><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">A correlation between two variables is a measure of association, but it does not indicate a causal relationship. This requires randomized controlled trials or other statistical methods.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/Fc6c\"><span style=\"font-weight: 400;\"><sup>4<\/sup><\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">When researchers want to know the answer to a scientific question, they create a hypothesis to be tested in a study.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/UouY\"><span style=\"font-weight: 400;\"><sup>2<\/sup><\/span><\/a><\/p>\n<p><span style=\"font-weight: 400;\">To this end, the P value and hypothesis testing theory are useful tools that help to plan an experiment, interpret the observed results, and report the findings to colleagues. However, it is essential that these tools are understood! In this way, interpretations and conclusions about the results are made based on plausible scientific premises and not just on the isolated evaluation of statistical analyses.<\/span><a href=\"https:\/\/paperpile.com\/c\/cZNHHk\/bBzr\"><span style=\"font-weight: 400;\"><sup>12<\/sup><\/span><\/a><span style=\"font-weight: 400;\"> And so, evidence brings us closer to scientific truths!<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b>How to cite this article:<\/b><\/p>\n<p><span style=\"font-weight: 400;\">KACHI. <\/span><i><span style=\"font-weight: 400;\">Evidence or Coincidence? Insights Into Statistics and the Scientific Method<\/span><\/i><span style=\"font-weight: 400;\">. S\u00e3o Paulo: KACHI Comunica\u00e7\u00e3o Cient\u00edfica, 24 Jun 2025. Available at:<\/span> <a href=\"https:\/\/kachi.com.br\/en\/evidence-or-coincidence-insights\"><span style=\"font-weight: 400;\">https:\/\/kachi.com.br\/en\/evidence-or-coincidence-insights<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p>Bibliographic References:<\/p>\n<ol>\n<li>Farrugia P, Petrisor BA, Farrokhyar F, Bhandari M. Practical tips for surgical research: Research questions, hypotheses and objectives. Can J Surg 2010;53(4):278\u201381.<\/li>\n<li>Miller J. Hypothesis Testing in the Real World. Educ Psychol Meas 2017;77(4):663\u201372.<\/li>\n<li>The BMJ. Correlation and regression [Internet]. [cited 2020 Aug 31];Available from: https:\/\/www.bmj.com\/about-bmj\/resources-readers\/publications\/statistics-square-one\/11-correlation-and-regression<\/li>\n<li>Hung M, Bounsanga J, Voss MW. Interpretation of correlations in clinical research. Postgrad Med 2017;129(8):902\u20136.<\/li>\n<li>Rothman KJ, Greenland S. Causation and Causal Inference in Epidemiology. Am J Public Health 2005;95(S1):S144\u201350.<\/li>\n<li>Altman N, Krzywinski M. Association, correlation and causation. Nat Methods 2015;12(10):899\u2013900.<\/li>\n<li>Keogh B, Monks T. The impact of delayed transfers of care on emergency departments: common sense arguments, evidence and confounding. Emerg Med J 2020;37(2):95\u2013101.<br \/>\nVigen T. Spurious correlations [Internet]). [cited 2020 Aug 31];Available from: https:\/\/www.tylervigen.com\/spurious-correlations<\/li>\n<li>Chang M. What Constitutes Science and Scientific Evidence: Roles of Null Hypothesis Testing. Educ Psychol Meas 2017;77(3):475\u201388.<\/li>\n<li>Tay MZ, Poh CM, R\u00e9nia L, MacAry PA, Ng LFP. The trinity of COVID-19: immunity, inflammation and intervention. Nat Rev Immunol 2020;20(6):363\u201374.<\/li>\n<li>Palesch YY. Some common misperceptions about P values. Stroke 2014;45(12):e244\u20136.<br \/>\nBiau DJ, Jolles BM, Porcher R. P value and the theory of hypothesis testing: an explanation for new researchers. Clin Orthop Relat Res 2010;468(3):885\u201392.<\/li>\n<li>Andrade C. The Value and Statistical Significance: Misunderstandings, Explanations, Challenges, and Alternatives. Indian J Psychol Med 2019;41(3):210\u20135.<\/li>\n<li>Di Leo G, Sardanelli F. Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach. Eur Radiol Exp 2020;4(1):18.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>We are constantly asking questions like: Is this vaccine effective in preventing the flu? Is this medication effective in treating COVID-19? Does this substance cause cancer? &nbsp; Clinical practice is grounded in scientific data and research findings. For medicine to evolve, the scientific community continuously asks relevant questions and conducts studies to answer them.1 &nbsp; [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":1154,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[79],"tags":[123,71,120,69,127,125,126],"class_list":["post-1143","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scientific-communication","tag-kachi-en","tag-pharmaceutical-marketing","tag-promotional-material","tag-scientific-communication","tag-scientific-content","tag-scientific-evidence","tag-statistics"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Evidence or Coincidence? 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