Konsep Statistika Inferensial, Hipotesis dan Pengujian Hipotesis, Taraf Signifikansi

Authors

  • Sulia Fitriani Universitas Pembangunan Panca Budi, Indonesia
  • Nazwa Salsabila Br Manurung Universitas Pembangunan Panca Budi, Indonesia
  • Dian Sri Anggraini Universitas Pembangunan Panca Budi, Indonesia
  • Hadi Saputra Panggabean Universitas Pembangunan Panca Budi, Indonesia

DOI:

https://doi.org/10.57235/aurelia.v4i2.6786

Keywords:

Inferential Statistics, Hypothesis Testing, Null Hypothesis (H₀), Alternative Hypothesis (H₁), Significance Level (α), P-value, Type I Error, Type II Error, One-tailed Test, Two-tailed Test, Bonferroni Correction, Holm–Bonferroni Method, Statistical Power

Abstract

Inferential statistics enables drawing conclusions about a population from sample data. Hypothesis testing involves formulating a null hypothesis (H₀) and an alternative hypothesis (H₁). A p-value indicates the probability of obtaining results at least as extreme as those observed, assuming H₀ is true. If the p-value is less than the predetermined significance level (α), commonly set at 0.05, H₀ is rejected in favor of H₁, suggesting statistical significance. Tests can be one-tailed or two-tailed, depending on the research question's directionality. Type I errors (false positives) and Type II errors (false negatives) are risks in hypothesis testing. Controlling these errors involves careful selection of α and consideration of the test's power, which is the probability of correctly rejecting a false null hypothesis. In studies involving multiple comparisons, adjustments such as the Bonferroni correction and the Holm–Bonferroni method are employed to control the family-wise error rate, thereby reducing the likelihood of Type I errors across multiple tests. These techniques adjust the significance thresholds to maintain the overall error rate within acceptable bounds.

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References

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Published

2025-07-31

Issue

Vol. 4 No. 2 (2025): July 2025

Section

Articles

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