How Quality Data is Ensured

How Quality Data is Ensured

How Quality Data is Ensured While challenges may arise, scientists are committed to ensuring they get the best data possible Common errors: Sampling bias: Where samples are collected might affect results. Method weaknesses: Some methods miss details. Errors following steps: Small mistakes can happen. Data upload errors: Mistakes when entering data. How to minimise mistakes: Clear instructions: Training and reminders help everyone understand the process. Reliable method design: Steps are planned carefully. Data checking: Review data after it’s collected and uploaded. By following these steps, The data collected can be used by scientists! What challenges could arise in citizen science projects, and how could they be addressed? Reflect