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All Journal Aurelia: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Journal of Humanities Education Management Accounting and Transportation
Fitriani, Sulia
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Physics Social Sciences Transportation

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Mengidentifikasi Perbedaan Individu yang Menyebabkan Perbedaan Proses dan Hasil Belajar Puspita, Laila Dwi; Hasibuan, Nuri Syamsika; Suciani, Anggun; Gs, Mila Fadilah; Fitriani, Sulia; Panggabean, Hadi Saputra
Journal of Humanities Education Management Accounting and Transportation Vol 2, No 1 (2025): Februari 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/hemat.v2i1.5086

Abstract

Setiap siswa memiliki karakteristik unik seperti kemampuan kognitif, gaya belajar, motivasi, dan latar belakang sosial yang membedakan cara mereka dalam belajar. Faktor-faktor tersebut tidak hanya berdampak pada proses belajar, tetapi juga pada hasil yang dicapai oleh siswa. Dengan mengidentifikasi perbedaan individu, pendidik dapat menerapkan berbagai strategi, termasuk metode pembelajaran yang bervariasi, fleksibilitas penugasan, pendekatan pembelajaran berdiferensiasi, dan pemanfaatan teknologi, guna menciptakan lingkungan belajar yang inklusif. Dengan demikian, setiap siswa memiliki kesempatan yang sama untuk berkembang sesuai potensi mereka. Makalah ini memberikan rekomendasi agar pendidik, lembaga pendidikan, dan pihak terkait lainnya lebih responsif terhadap kebutuhan belajar siswa yang beragam, sehingga hasil pembelajaran dapat lebih optimal.
Konsep Statistika Inferensial, Hipotesis dan Pengujian Hipotesis, Taraf Signifikansi Fitriani, Sulia; Manurung, Nazwa Salsabila Br; Anggraini, Dian Sri; Panggabean, Hadi Saputra
AURELIA: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Vol 4, No 2 (2025): July 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/aurelia.v4i2.6786

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.
Konsep Statistika Inferensial, Hipotesis dan Pengujian Hipotesis, Taraf Signifikansi Fitriani, Sulia; Manurung, Nazwa Salsabila Br; Anggraini, Dian Sri; Panggabean, Hadi Saputra
AURELIA: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Vol 4, No 2 (2025): July 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/aurelia.v4i2.6786

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.
Co-Authors Anggraini, Dian Sri Gs, Mila Fadilah Hasibuan, Nuri Syamsika Manurung, Nazwa Salsabila Br Panggabean, Hadi Saputra Puspita, Laila Dwi Suciani, Anggun