5 hours ago
https://www.eurekalert.org/news-releases/1151475
INTRO: Even researchers and people with extensive training in statistics often find it difficult to interpret statistical results correctly. This is shown by a new systematic review carried out by Uppsala University and others, in which researchers have analysed 55 studies involving over 15,000 participants.
Statistics are an important tool in many scientific fields. Using statistical methods, researchers can not only describe what has been observed but also draw more general conclusions. For example, this might involve investigating whether a medicine is effective or whether there is a link between air pollution and human health.
Drawing conclusions from statistical results is known as statistical inference. The way in which the results of statistical analyses are described and understood influences the conclusions that are actually drawn. Concepts such as the p-value, statistical significance and confidence intervals play a central role here.
But how well do we actually understand these concepts?
“We have found many studies that have examined this in various ways. What’s striking is that, despite using different methods, the researchers have reached such similar conclusions: understanding of what can actually be inferred from statistical results is inadequate and misunderstandings are common,” says Per Lytsy, affiliated researcher at the Department of Public Health and Caring Sciences, Uppsala University. (MORE - no ads)
INTRO: Even researchers and people with extensive training in statistics often find it difficult to interpret statistical results correctly. This is shown by a new systematic review carried out by Uppsala University and others, in which researchers have analysed 55 studies involving over 15,000 participants.
Statistics are an important tool in many scientific fields. Using statistical methods, researchers can not only describe what has been observed but also draw more general conclusions. For example, this might involve investigating whether a medicine is effective or whether there is a link between air pollution and human health.
Drawing conclusions from statistical results is known as statistical inference. The way in which the results of statistical analyses are described and understood influences the conclusions that are actually drawn. Concepts such as the p-value, statistical significance and confidence intervals play a central role here.
But how well do we actually understand these concepts?
“We have found many studies that have examined this in various ways. What’s striking is that, despite using different methods, the researchers have reached such similar conclusions: understanding of what can actually be inferred from statistical results is inadequate and misunderstandings are common,” says Per Lytsy, affiliated researcher at the Department of Public Health and Caring Sciences, Uppsala University. (MORE - no ads)