What are the best ways to use statistics to help your team make informed decisions?

Before stepping into data analysis, the researcher should clearly observe the current loopholes in the current processes. Then, he should clearly construct the questions , which should be addressed via analysis of data. This helps to reduce the time and effort spent in implementing statistical techniques

L'analyse de données, demande une hypothèse claire ! Les Stats sont uniquement des outils pour confirmer ou infirmer votre hypothèse ! Mais surtout pas de formuler une question après coup ! Enfin, la meilleur façon de procéder est de regarder ses données sous différents angles. Series temporelles, histogramme. Ensuite, vous décidez des outils les plus pertinents pour tester votre hypothèse. Très souvent, les approches statistiques se croisent avec l'analyse du signal ne l'oubliez pas ! Quelques outils de filtrage ou de transformation sont quelques fois bien utiles et vous évitent bien des analyses souvent trop complexes. Et pour comprendre quelque chose à vos données évitez les réseaux de neurones, ce n'est pas fait pour ça !

Also, avoid false representation. Some examples include 1. Using SEM instead of SD, to reduce the size of error bars. Note that SEM is standard error of Mean. 2. x-limits abbreviated to different dimensions across treatments/ outcomes that is under comparison. (false impression of positivity/negativity) 3. Post-hoc analysis conducted, when the primary analysis failed to pass statistical significance. 4. Parametric test utilized, when the data is skewed/ non-parametric. 5. Statistical test are not appropriate as per study design. For e.g. if an outcome is measured repeatedly across two treatments and the outcome is parametric, the best way to assess overall signficance is to conduct a two-way repeated measures ANOVA.

Using statistics effectively in decision-making involves choosing the right methods based on the data and the question at hand. Descriptive statistics can summarize and describe data features, while inferential statistics, including hypothesis testing and regression analysis, can help draw conclusions or predict trends. It's crucial to consider the confidence level and significance desired, and be aware of potential biases and errors. For example, in assessing customer satisfaction, descriptive statistics might reveal average satisfaction levels, while inferential statistics could determine if differences in satisfaction across demographics are statistically significant, guiding targeted business strategies.

Effectively communicating statistical findings is crucial in aiding team decision-making. Data visualization plays a key role in this process. It involves using charts, graphs, or dashboards to clearly and engagingly present the results. The choice of visualization depends on the audience and the specific insights you wish to convey. Ensure your visualizations are well-labeled and include titles, legends, and captions for clarity. For instance, a well-crafted bar chart can succinctly show sales performance across different regions, making it easier for the team to identify trends and make data-driven decisions. Effective visualization turns complex statistical data into accessible and actionable insights.

Sometimes, the data you're given doesn't match the data necessary to answer the question being asked. It's important to review the data closely and to let the stakeholder(s) know if it does not meet their needs.