ERRORS IN ANACHEM

• Errors, Random Errors, and Statistical Data in Chemical Analysis:

  • Analytical results are not free of errors or uncertainties
  • Multicellular organisms require specialized exchange surfaces for efficient gas exchange due to a higher surface area to volume ratio
• Measurements are always accompanied by uncertainty, and the true value falls within a range due to uncertainty
• Reliability can be assessed in several ways:

  • Analyzing standards of known composition and comparing results
  • Calibrating equipment to enhance data quality
• Measures of Central Tendency:

  • Mean, Median, and Mode describe the center of a set of data
  • Mean is the sum of all values divided by the number of replicates
  • Median is the middle result when data are arranged in order of size
  • Mode is the most frequently appearing value in a set of data
• Measures of Dispersion:

  • Range, Standard Deviation, and Variance describe how widely dispersed data are in a dataset
  • Range is the difference between the largest and smallest values
  • Standard Deviation describes the spread of individual measurements about the mean
  • Variance is the square of the standard deviation
• Accuracy and Precision:

  • Accuracy is the closeness of a measurement to the true or accepted value
  • Precision is the spread of data about a central value and can be repeatability or reproducibility
• Types of Errors:

  • Systematic/Determinate errors affect accuracy
  • Random errors/Indeterminate errors cause data to be scattered around a mean value
  • Gross errors/Blunders are occasional large errors that lead to outliers
• Systematic errors in analytical methods can be difficult to detect and are the most serious type of error
• Personal errors in measurements involve personal judgments and can lead to systematic, unidirectional errors
• Personal errors can be influenced by factors like color blindness, number bias, and physical disabilities
• Systematic errors can be constant or proportional, with constant errors not depending on the size of the quantity measured
• Systematic instrument errors are usually corrected by periodic calibration, while personal errors can be minimized by care and self-discipline
• Analyzing standard reference materials is a way to estimate the bias of an analytical method
• Standard reference materials can be analyzed by validated methods, multiple measurement methods, or a network of competent laboratories
• Using an independent analytical method can help detect errors in the method being evaluated
• Blank determinations and varying sample sizes are methods to detect constant errors in analytical methods
• Random errors in analytical results can be evaluated using statistical methods
• Statistical analysis of analytical data is often based on the assumption that random errors follow a Gaussian distribution
• The significant figure convention is used to indicate the probable uncertainty associated with an experimental measurement
• Rounding data and reporting computed data are important steps in presenting analytical results accurately