• What Data to Record? 📝

  • Raw data (not just the averages).
  • Should answer your research question.
  • Examples: Heart rate readings, time after exercising, pre-exercise heart rate, room temperature.

• Precision is 🔑

  • Record data with precision, e.g., 5.5cm, 8.5cm.

• Eyes Open for Additional Observations 🧐

  • Things like participant age, gender, athletic background, resting posture.
  • Sometimes, it's hard to analyze this data (it's qualitative).

• Group Projects, Beware! 👥

  • Using class data? Clearly present YOUR own data.
  • Don't just copy-paste someone else's table. That's a no-no.

🌍 Real-world Example: Think of it like a fitness tracker. When you go for a run, it records data every second, not just your average heart rate.

• Data Processing = Data Transformation

  • Turn raw data into something presentable: graphs, written info, or even verbal.

• Processed Data Display

  • Can be with raw data, but be CLEAR about which is which.
  • Keep the same precision.

• Spotting the Odd Ones Out 👀

  • Anomalous results? Highlight or remove them.
  • But always explain your choice!

🌍 Real-world Example: Like taking your daily steps from the fitness tracker and then finding out your weekly average. If you hiked a mountain one day, that might be an "anomalous" high step count.