  Increasing attention has been drawn to the misuse of statistical methods over
recent years, with particular concern about the prevalence of practices such as
poor experimental design, cherry-picking and inadequate reporting. These
failures are largely unintentional and no more common in ecology than in other
scientific disciplines, with many of them easily remedied given the right
guidance.
  Originating from a discussion at the 2020 International Statistical Ecology
Conference, we show how ecologists can build their research following four
guiding principles for impactful statistical research practices: 1. Define a
focused research question, then plan sampling and analysis to answer it; 2.
Develop a model that accounts for the distribution and dependence of your data;
3. Emphasise effect sizes to replace statistical significance with ecological
relevance; 4. Report your methods and findings in sufficient detail so that
your research is valid and reproducible.
  Listed in approximate order of importance, these principles provide a
framework for experimental design and reporting that guards against unsound
practices. Starting with a well-defined research question allows researchers to
create an efficient study to answer it, and guards against poor research
practices that lead to false positives and poor replicability. Correct and
appropriate statistical models give sound conclusions, good reporting practices
and a focus on ecological relevance make results impactful and replicable.
  Illustrated with an example from a recent study into the impact of
disturbance on upland swamps, this paper explains the rationale for the
selection and use of effective statistical practices and provides practical
guidance for ecologists seeking to improve their use of statistical methods.

Methodology;Populations and Evolution;Applications