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Uncertainties and Methods Simplified Revision Notes

Revision notes with simplified explanations to understand Uncertainties and Methods quickly and effectively.

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Uncertainties and Methods

SI Units and Their Prefixes

  • Fundamental SI Units include:
    • Mass (m): kilogram (kg)

    • Length (l): metre (m)

    • Time (t): second (s)

    • Electric Current (I): ampere (A)

    • Temperature (T): kelvin (K)

    • Amount of Substance (n): mole (mol) Units for derived quantities, like force and voltage, can be obtained from these base units. For example:

    • Force (F=ma)(F = ma): kg·m/s² or N (newton)

    • Voltage (V=E/Q)(V = E/Q) : derived as V=kgm2As3V = \frac{kg \cdot m^2}{A \cdot s^3}

  • SI Prefixes scale units by powers of ten. Common prefixes include:
    • kilo (k): 10310^3
    • mega (M): 10610^6
    • milli (m): 10310^{-3}
    • micro (µ): 10610^{-6}
lightbulbExample

Example Conversions:

  • To convert energy in electron volts (eV) to joules (J), use 1eV=1.6×1019J1 \, \text{eV} = 1.6 \times 10^{-19} \, \text{J}.
  • Converting kWh to joules: 1kWh=3.6×106J1 \, \text{kWh} = 3.6 \times 10^6 \, \text{J}.

Limitation of Physical Measurements

Types of Errors:

  1. Random Errors:
  • Affect precision and cause scattered readings.
  • Example: electronic noise in measurement.
  • Minimising Random Errors:
  • Take multiple measurements and calculate the mean.
  • Use digital equipment (e.g., data loggers).
  • Choose appropriate tools (e.g., a micrometer for small lengths).
  1. Systematic Errors:
  • Affect accuracy by consistently skewing results in one direction.
  • Example: zero error on a balance.
  • Reducing Systematic Errors:
  • Calibrate instruments regularly.
  • Correct for background radiation in radiation measurements.
  • Measure liquids at eye level to avoid parallax errors.
infoNote

Key Terms:

  • Precision: Consistency of measurements.
  • Accuracy: Closeness to the true value.
  • Resolution: Smallest measurable change.
  • Repeatability: Same experimenter, same method, same results.
  • Reproducibility: Different experimenter/method, same results.

Types of Uncertainty:

  • Absolute Uncertainty: Fixed uncertainty ((e.g., ±0.5mm)\pm 0.5 \, \text{mm} ).
  • Fractional Uncertainty: Absolute uncertainty divided by the measurement ((e.g., 0.510)\frac{0.5}{10} ).
  • Percentage Uncertainty: Fractional uncertainty as a percentage ((e.g., 0.510×100%)\frac{0.5}{10} \times 100\% ).

Calculating and Reducing Uncertainty

  • Reduce Percentage/Fractional Uncertainty by measuring larger quantities when possible (e.g., a longer rope).

  • Uncertainty in a Reading: ±\pm half the smallest scale division (e.g.,±0.5°C \pm 0.5 \, \text{°C}) for a thermometer with 1°C1°C divisions).

  • Uncertainty for Multiple Readings: ±\pm half the range (e.g., if measurements range from 6262 to 6464, the uncertainty is ±1\pm 1 ). Combining Uncertainties:

  • Addition/Subtraction: Add absolute uncertainties.

  • Multiplication/Division: Add percentage uncertainties.

  • Raising to a Power: Multiply percentage uncertainty by the power.

lightbulbExample

Example Calculation: If the radius of a circle is 5.0±0.3cm5.0 \pm 0.3 \, \text{cm}, the percentage uncertainty in the area A=πr2A = \pi r^2 would be:

  • Percentage uncertainty in rr = 0.35.0×100%=6%\frac{0.3}{5.0} \times 100\% = 6\%
  • Area uncertainty = 6%×2=12%6\% \times 2 = 12\% since area depends on r2r^2.

Uncertainties and Graphs

  • Represent uncertainties with error bars on graphs.

  • A line of best fit should go through all error bars (excluding outliers). Finding Uncertainty in Gradients:

  • Draw steepest and shallowest lines within error bars.

  • Calculate the difference in gradients to find percentage uncertainty:

Percentage uncertainty=best gradient - worst gradientbest gradient×100%\text{Percentage uncertainty} = \frac{\text{best gradient - worst gradient}}{\text{best gradient}} \times 100\%

Estimation of Physical Quantities

  • Orders of Magnitude: Power of ten approximations for size comparison (e.g., nucleus diameter is around 1014m10^{-14} \, \text{m}). Experiment Design:

  • Variables:

    • Independent: What you change.
    • Dependent: What you measure.
    • Control: Variables that stay constant.
  • Data Types:

    • Discrete: Specific values (e.g., count of objects).
    • Continuous: Range of values (e.g., temperature).
    • Categorical: Qualitative data (e.g., colour).

Graphing Relationships:

  • Linear Relationships: For y=kxy = kx, plot yy against xx for a straight line.
  • Reciprocal Relationships: For y=kxy = \frac{k}{x}, plot yy against 1x\frac{1}{x}.
image image image

Logarithmic and Exponential Functions:

  • Convert equations like R=kAnR = kA^n into a linear form by taking logarithms:
lnR=nlnA+lnk\ln R = n \ln A + \ln k

Plotting lnR\ln R against lnA\ln A gives a straight line with gradient nn.

image
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