Regarding how we use quantitative approaches to counter the human tendency to find patterns or associate casuality where none exists, including thoughts about significance testing in ecology and critics by the general public, respond thoughtfully to the following post and agree or disagree with the statements to add to or enhance the discussion (Fully cite all resources in-text from recent peer-reviewed scientific journals and provide a reference section at the end) :

Discussion - Implementing Quantitative Approaches

When it comes to humans, we have a tendency to believe or associate things due to the way our brain is hardwired. Our brains tend to create an illusion based on our perception to maintain that sense of security. Like how in Michael Shermer video, he showed some images and in some of them you saw a figure based on your brain's perception of past images, like a brain algorithm. To prevent wrong associations from occurring, methods based on estimates to stimulate your brain and maintain its focus. It would not be right to say that it is a problem to base management on patterns that are intuitive, when you have experienced similar scenarios and the outcome is the same your intuition becomes more assertive, however, in management is better to be save than sorry which is why testing such intuitions are necessary to limit uncertainty and risks.

In ecology there are traditional testing based on frequent statistics which is where the Null hypothesis comes in. Understanding how to read and understand null hypothesis is often difficult due to misinterpretation, having small or too big parameters can create poor precision on your final analysis, therefore, when there is uncertainty significance tests like NHST can be proven useful in order to decided the right amount needed and if a data should be kept or dropped due to being "not significant" (Burnham et al., 2001), creating a strong inference. Therefore, I believe that significance testing is necessary to create more accurate results and testing the data accumulated without dropping to conclusions, providing surveys or experiments certainty (Johnson 1999).

Science involves estimation-prediction-understanding and uncertainty into estimates of precision hypothesis testing differ from model selection criteria. For example, the method of information theoretic approach can help on research or conservation of birds since it provides a full analysis of data and estimates of precision instead of mere observation and hypothesis (Gerrodette 2011). In regards to science, there will always be a tendency to association, without assumptions most studies are not completed and in some cases the results shown may differ depending on the person analyzing it which is why acknowledging their assumptions behind their analysis is important. In addition, other studies may arise from the assumptions created from the analysis,Importance sampling is used in many areas of modern econometricsto approximate unsolvable integrals (koopman et al., 2009).