When choosing a statistical test to use, it is vital that the data meets the assumptions required for the test to be appropriate. Checking these assumptions is good practice and allows the researcher to analyse the data in the way that is most appropriate.

On the whole, a better general understanding and value of uncertainty allows for a more scientifically literate society that values truths over “certainties”.

Rather than allowing the word “uncertainty” to diminish the power of results, it empowers scientists to openly discuss and acknowledge the limits of their findings. This creates a culture of trust, rigour and transparent communication within the community.

Uncertainty is a compelling force that steers scientific innovation. Uncertainty emphasises areas of weakness or gaps in knowledge, catalysing further research in these areas.

It is important that we as scientists know how to talk about these different types of uncertainty appropriately. It is an intrinsic pillar of scientific inquiry to represent the limitations in knowledge, measurement and the “known unknowns” rather than a lack of understanding altogether.

Rather than allowing the word “uncertainty” to diminish the power of results, it empowers scientists to openly discuss and acknowledge the limits of their findings. This creates a culture of trust, rigour and transparent communication within the community.

When the community evaluates work directly, a paper's value is no longer determined by the journal that accepted it or the citation algorithm that surfaced it. It's determined by scientists, the people best placed to judge it.

Publish-or-perish has produced rushed research, unreproducible results, and careers spent optimising for the wrong things.

Academia has long conflated journal prestige with scientific value. But, when researchers evaluate work directly, the metrics that have distorted research culture start to lose their grip. The community decides what's worth reading, and building on, and the work that actually holds up gets rewarded.

Science should be accessible to readers, but also publishing should be accessible to authors. Data should be accessible to all to maintain quality and accountability.

What would you like to see change in the world of scientific publishing?

Correctly executed statistical tests wield incredible power. Inappropriately executed statistical tests can be incredibly dangerous, wasting time, money and potentially putting lives at risk.

As scientists, we need to exercise critical thinking every time we read a paper, not just take things at face value.

Honest insignigicant data > falsified significant data.

We need to value genuine expertise. Expert peer reviewers should be recognised for their efforts, and we should always view what we are reading through a critical lense.

Reproducibility and transparency are more important than p-values. It's more important to be honest about negative data than to lie about positive data.

Science needs to be continuously scrutinised even after publishing. Keep all areas of science accountable.

People should read and critique science at its source to form their own opinions. Not rely on how science is reported for political gain.

Scientific transparency and rigour should be more important than positive data. It's what keeps science moving forward.

Our first sprint is now live! 🙌 May is Mental Health Awareness Month so we are rating and reviewing 5 top cited papers in depression research, come and join the conversation! buff.ly/jjjpSWo

Join us in the starting blocks for our first sprint tomorrow. #paperstars #mentalhealth #mentalhealthawarenessmonth #sciencequality

It's time to acknowledge good-quality science, no matter the citation count or the prestige of the journal.

Get ready for our first sprint... #paperstars #mentalhealthawarenessmonth #sciencequality #mentalhealth #sprint

Join us in our mission to uncouple quality science from the h-index.

Pleased to share this recent interview about our mission at @paperstarsorg.bsky.social to uncouple science from publication metrics by reshaping how science is judged prelights.biologists.com/news/goodrea...

'Goodreads for Science': How Paperstars reimagines the way science should be judged - preLights

Paperstars was developed to help refocus research culture on doing good science, rather than on where papers are published. Founded by Danny Schnitzler and Jenna Stephen, the platform aims to decouple...

prelights.biologists.com

Quality science should be free from the pressures of the publication game.

All data is valuable. Whether positive or negative, both are valid.

Published ≠ high quality That's for you to determine.

Let's focus on quality science, regardless of p-values, significance, or "sexiness".

Scientists deserve to conduct their work free from the pressures of the h-index.