How can data analysis lead to misleading conclusions?
Data analysis involves statistical techniques to interpret the collected data. Misleading conclusions can arise from:
P-hacking: Manipulating data or performing multiple analyses until statistically significant results are found. Overfitting: Creating a model that describes random noise rather than the underlying relationship. Underpowered Studies: Studies with insufficient sample sizes may not detect true associations, leading to false negatives. Multiple Comparisons: Conducting numerous statistical tests increases the risk of finding a significant result by chance.
