Foundations of Physics

• SI unit for temperature

  Kelvin (K)
• SI prefixes


  • Pico (p) 1 x 10^-12
  • Nano (n) 1 x 10^-9
  • Micro 1 x 10^-6
  • Milli (m) 1 x 10^-3
  • Centi (c) 1 x 10^-2
  • Deci (d) 1 x 10^-1
  • Kilo (k) 1 x 10^3
  • Mega (M) 1 x 10^6
  • Giga (G) 1 x 10^9
  • Tera (T) 1 x 10^12
• SI unit


  A set of base units for physical quantities from which other units are derived
• SI derived unit


  A unit of measurement derived from SI base units
• Homogeneity of units


  The units in any equation must always be the same on both sides
• Systematic errors


  Systematic errors are the same every time you repeat the experiment. They may be caused by the equipment you're using or how it's set up.
• Random errors


  They're what makes the results a bit different each time you repeat an experiment.
• Reducing random errors


  • Using more sensitive apparatus so your results can be more precise, and repeating measurements can reduce the effect of random errors
• Scalar quantity


  A quantity that has only magnitude
• Vector quantity


  A quantity that has both magnitude and direction
• Drawing vectors


  'Tip to tail'
• Calculating resultant vector for vectors at right angles

  Pythagoras' theorem and trigonometry
• Calculating resultant vector for vectors not at right angles

  Draw a scale diagram
• Combining uncertainties when adding or subtracting data

  Add the absolute uncertainties
• Calculating uncertainty


  Absolute error/measured value
• Calculating percentage uncertainty


  Uncertainty/measured value x 100
• Combining uncertainties when multiplying or dividing data

  Add the percentage uncertainties
• Combining uncertainties when raising to a power

  Multiply the percentage uncertainty by the power
• Percentage difference


  How close your answer is to the True Value
• Absolute uncertainty


  The total uncertainty for a measurement
• Percentage error


  The uncertainty given as a percentage of the measurement
• Repeatability


  You can repeat it multiple times and get the same results. For experiments, doing more repeats enables you to assess how precise your data are - the more repeats you do, and the more similar the results of each repeat are, the more precise your data.
• Reproducibility


  If someone else can recreate your experiment using different equipment or methods, and gets the same results you do, the results are reproducible
• Assumed uncertainty when no uncertainty is given

  Half the increment of the last significant figure that the value is given to. E.g. 2.0 is given to 2 significant figures, so you would assume an uncertainty of 0.05
• Valid result


  A valid result answers the original question, using precise data. If you haven't controlled all the variables your results won't be valid, because you won't just be testing the effect of the independent variable
• Accurate result


  One that's really close to the true answer. If you're measuring something like g, which has been tested many times, and is known to a good degree of certainty, you can assess how accurate your results are by comparing them to this value. You can't assess the accuracy of a result if your measuring something that's unknown or has never been measured before.
• The less random error there is in the measurement


  The more precise your results
• Reducing random error in an experiment

  • Increasing the number of repeats
  • Using the most appropriate equipment
• Calculating uncertainty of final results from a line of best fit

  The uncertainty in the gradient is given by the difference between the best gradient and the worst gradient
• Calculating the uncertainty in the y-intercept on a graph

  It is the difference between the best and worst intercepts
• Examples of scalar quantities

  • Mass
  • Time
  • Temperature
  • Length
  • Speed
  • Energy
• Examples of vector quantities

  • Displacement
  • Force
  • Velocity
  • Acceleration
  • Momentum
• Resolving a vector into horizontal and vertical components

  Draw a scale diagram