The article critiques the use of statistical significance testing in wildlife research, arguing that it often leads to misinterpretation and is not as valuable as commonly believed. It highlights the arbitrary nature of P-values, the incorrect interpretation of statistical significance, and the limitations of hypothesis testing in ecological contexts. The author explains that statistical hypothesis testing typically assumes a null hypothesis that is known to be false in advance, which contrasts with scientific hypothesis testing, which examines credible hypotheses about natural phenomena. The paper discusses alternative approaches such as estimation, confidence intervals, decision theory, and Bayesian methods, which provide more meaningful insights into data and are better suited for ecological research. It also addresses the misuse of power analysis and the confusion between statistical and biological significance. The author argues that statistical hypothesis testing is often overused and misapplied, and that more appropriate methods should be adopted to improve the quality of ecological research. The paper emphasizes the importance of understanding the limitations of statistical tests and the need for more nuanced approaches to data analysis in wildlife science.The article critiques the use of statistical significance testing in wildlife research, arguing that it often leads to misinterpretation and is not as valuable as commonly believed. It highlights the arbitrary nature of P-values, the incorrect interpretation of statistical significance, and the limitations of hypothesis testing in ecological contexts. The author explains that statistical hypothesis testing typically assumes a null hypothesis that is known to be false in advance, which contrasts with scientific hypothesis testing, which examines credible hypotheses about natural phenomena. The paper discusses alternative approaches such as estimation, confidence intervals, decision theory, and Bayesian methods, which provide more meaningful insights into data and are better suited for ecological research. It also addresses the misuse of power analysis and the confusion between statistical and biological significance. The author argues that statistical hypothesis testing is often overused and misapplied, and that more appropriate methods should be adopted to improve the quality of ecological research. The paper emphasizes the importance of understanding the limitations of statistical tests and the need for more nuanced approaches to data analysis in wildlife science.