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Hypothesis Testing (t-Test) definitions

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  • t-Test
    Statistical method for comparing means of populations when the population standard deviation is unknown and sample size is small.
  • t Score
    Numerical value indicating how much a sample mean deviates from a population mean, scaled by sample variability and size.
  • Sample Average
    Arithmetic mean calculated from a set of measurements, representing the central value of the sample data.
  • Population Average
    Expected value or mean of an entire group from which a sample is drawn, often denoted as Mu.
  • Standard Deviation
    Measure of data spread within a set, indicating how much individual values differ from the mean.
  • Variance
    Statistical quantity representing the squared standard deviation, reflecting data dispersion.
  • Degrees of Freedom
    Number of independent values in a calculation, often linked to sample size and used in statistical tests.
  • Pooled Standard Deviation
    Combined measure of variability from two samples, used when their variances are assumed equal.
  • Paired Data
    Data sets where each value in one group is uniquely matched to a value in another, often from different methods.
  • t Table
    Reference chart listing critical values for the t distribution, used to assess statistical significance.
  • Significant Difference
    Result indicating that observed differences between means are unlikely due to random chance, based on statistical criteria.
  • Confidence Interval
    Range of values, derived from sample data, likely to contain the true population parameter with a specified probability.
  • Equal Variance
    Condition where two populations are assumed to have the same variability, affecting the choice of t-test formula.
  • Unequal Variance
    Situation where two populations have different variability, requiring a modified t-test approach and degrees of freedom.
  • Sample Size
    Total number of measurements or observations in a data set, influencing statistical calculations and test selection.