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Two Proportions Hypothesis Test - Excel definitions

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Termini in questo insieme (15)

  • Null Hypothesis
    Assumes no difference between two population proportions, serving as the baseline for statistical testing.
  • Alternative Hypothesis
    Represents the claim being tested, suggesting a difference or inequality between two population proportions.
  • Sample Proportion
    Calculated by dividing the count of successes in a sample by the total sample size, reflecting observed frequency.
  • Pooled Proportion
    Combines data from both samples to estimate a shared proportion, used in the denominator of the z-score formula.
  • Z-Score
    Measures how far the difference between sample proportions deviates from the expected value under the null hypothesis.
  • P-Value
    Probability of observing a result as extreme as the test statistic, used to assess evidence against the null hypothesis.
  • Alpha
    Threshold for statistical significance, commonly set at 0.05, guiding the decision to reject or retain the null hypothesis.
  • Test Statistic
    Numerical value calculated from sample data, used to determine the likelihood of the observed outcome under the null hypothesis.
  • COUNTIF
    Excel function that counts cells meeting a specified criterion, useful for tallying categorical responses in data analysis.
  • NORM.S.DIST
    Excel function that returns the cumulative probability for a standard normal distribution, converting z-scores to p-values.
  • Sample Size
    Total number of observations in each group, essential for calculating proportions and variances.
  • Q Bar
    Represents one minus the pooled proportion, used in variance calculations for the z-score denominator.
  • Left Tail Probability
    Area under the normal curve to the left of the test statistic, relevant for one-sided hypothesis tests.
  • Categorical Data
    Data type representing distinct groups or categories, such as yes/no responses, analyzed in proportion tests.
  • Inferential Statistics
    Branch of statistics focused on drawing conclusions about populations based on sample data and hypothesis testing.