#Earthquake 29 km NW of #Suez (#Egypt) at 03:00 AM #Cairo local time Colored dots represent local shaking & damage level reported by eyewitnesses.
#زلزال#مصر
Dr Lucylynn Lizarondo provides an overview of 'Methods for data extraction and data transformation in convergent integrated mixed methods systematic reviews'. The published paper is available at: https://t.co/FQV6qyM2RZ
#JBIMethodology
#AI is Destroying the #University and Learning Itself. #Students use AI to write papers, #professors use AI to grade them, degrees become meaningless, & AI companies make fortunes. Welcome to the death of higher education. https://t.co/CwRLW2Cul8
🎉 Celebrating 20 years of impact for women, children & adolescents!
PMNCH has grown into the world’s largest alliance for #WCAH, driving rights-based progress & amplifying partner voices.
Explore our history of accomplishments, milestones & partner voices! 👇
https://t.co/PeUlrREzl0
Non-adherence to surgical antibiotic prophylaxis guidelines: findings from a mixed-methods study in a developing country https://t.co/oRL93Uxa3x #medRxiv
Mixed methods systematic reviews have become increasingly popular as they provide an innovative approach for addressing important questions in #EBHC.
Watch Dr @jbi_cindy summarise the importance of mixed methods systematic reviews: https://t.co/kZsVbE44mN
#JBImethodology
Misuse of p-values is a prevalent issue in scientific research. P-values are often misunderstood and misapplied, leading to incorrect conclusions. A p-value measures the probability of obtaining results at least as extreme as the observed ones, assuming that the null hypothesis is true. Common problems with p-values include:
✅ Overemphasis on significance: Researchers often focus on whether p-values are below a threshold (e.g., 0.05), ignoring the effect size and practical significance.
✅ P-hacking: Manipulating data or experimental conditions to achieve statistically significant p-values.
✅ Misinterpretation: Believing that a low p-value proves the alternative hypothesis or that a high p-value confirms the null hypothesis.
✅ Ignoring context: Failing to consider the broader context of the study, including prior evidence and the research design.
The graph shown in this post is a modified version of this Wikipedia image: https://t.co/C6zzlHi7KS
Want to deepen your understanding of statistics with R? Sign up for my online course, "Statistical Methods in R."
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#datasciencetraining #programming #DataVisualization #Data #DataAnalytics