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AQA A-Level Computer Science common mistakes

Binary number system common mistakes

Study Binary number system with curriculum-aligned Common Mistakes resources, practice links, and exam-focused support.

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common mistakes

Resource type

Topic

Binary number system

AqaA LevelComputer ScienceFundamentals of data representation

Common mistakes

  • Using the wrong maximum-value formula

    Calculating the maximum unsigned value for n bits as 2^n rather than 2^n - 1.

    Fix itUse 2^n - 1. There are 2^n possible bit patterns, but the values start at 0, so the largest value is one less than 2^n.

  • Forgetting positional shifts in multiplication

    Writing every partial product underneath the same starting position.

    Fix itShift each partial product left according to the position of the corresponding 1 in the multiplier, then add the aligned rows.

  • Forgetting the final addition of 1

    A student inverts the bits of a positive value and stops, treating the inverted pattern as its negative.

    Fix itAfter inverting every bit, always add 1, using the specified fixed number of bits. For example, +5 as 8 bits is 00000101, so -5 is 11111011, not 11111010.

  • Treating fractional bits as whole-number bits

    Reading the bits after the binary point as 1, 2, 4 and 8 instead of 1/2, 1/4, 1/8 and 1/16.

    Fix itWrite the place values above the fractional bits before calculating their contribution.

  • Assuming floating point is always exact

    Claiming that floating-point representation removes rounding errors.

    Fix itFloating point can still be inaccurate because a value may not be representable as a binary fraction in the available number of bits.

  • Using the stored value as the denominator

    Calculating relative error by dividing the absolute error by the stored value.

    Fix itDivide the absolute error by the actual value: relative error = absolute error / actual value.

  • Assuming floating point always has greater precision

    Stating that floating point is always more precise than fixed point.

    Fix itFloating point primarily offers a wider range. Its precision is variable, so it does not automatically provide greater precision for every value.

  • Changing the exponent in the wrong direction

    A learner moves the binary point to the right and increases the exponent.

    Fix itMoving the point right increases the mantissa by a power of two, so decrease the exponent by the same number of places. Moving the point left requires the exponent to increase.

  • Reversing the two conditions

    Describing overflow as a result that is too small, or underflow as a result that is too large.

    Fix itRemember that overflow is above the maximum representable value, while underflow is below the minimum representable value.

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