chemical measurements

• ERROR:


  In experiments, it's the difference between your result & the expected value.
• The two main error types:

  • random errors
  • systematic errors
• Random Errors:


  When measured values vary randomly around the true value.

  • Common in experiments due to HUMAN ERROR & RANDOM VARIATIONS.
  • By taking many measurements & calculating the MEAN, we can overcome the issue of random errors.
• What might cause random errors:

  • Trouble reading the scale of an instrument just right.
  • The person taking the reading makes a mistake.
  • The environment changes, like the lab getting warmer or the air moving around.
• Systematic Errors:


  When an issue in experimental design or EQUIPMENT causes the measured value to be consistently too high or consistently too low.

  • These errors are harder to identify.
• Examples of systematic errors:

  • Forgetting to reset a scale to zero, leading to all your weights being off.
  • Not reading a measurement at the correct eye level, so it seems smaller or larger.
• Uncertainty:


  About how confident you are in your measurements.

  • It's a number that tells you how much your results might be off by.

  
• With ANALOGUE INSTRUMENTS:


  Uncertainty is usually HALF of the smallest thing you can measure on it.

  • E.g. If your ruler's smallest division is 1 mm, your uncertainty is ±0.5 mm
• With Digital Instruments:

  Like a digital clock, it's simpler: uncertainty is just the SMALLEST NUMBER it can display.

  
• For results that are obtained from a series of REPEATED EXPERIMENTS, the uncertainty is ± HALF of the range of the results.
• Uncertainty in results can be estimated by:

  • Calculating the MEAN average & then determining the deviation of the highest & lowest results from the mean value.
  • An alternative method is to calculate the range of the results & DIVIDE this value by 2.
• Countryside data has smallest values, so 2 is a higher percentage of the value.