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Review on Problems with Null Hypothesis Significance Testing in Dental Research and Its Alternatives

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À̱¤Èñ ( Lee Kwang-Hee ) - ¿ø±¤´ëÇб³ Ä¡°ú´ëÇÐ ¼Ò¾ÆÄ¡°úÇб³½Ç

Abstract

Ä¡ÀÇÇÐ ¿¬±¸¿¡¼­ »ç¿ëµÇ´Â ±Í¹«°¡¼³ À¯ÀǼº °ËÁ¤¿¡¼­ p°ªÀ» ±âÁØÀ¸·Î ¿¬±¸ÀÇ °á°ú¸¦ Æò°¡ÇÏ´Â °ÍÀº ¸¹Àº ¹®Á¦Á¡À» ³»Æ÷ÇÏ°í ÀÖ´Ù. ±Í¹«°¡¼³ÀÌ ±â°¢µÇÁö ¾ÊÀº °æ¿ì¿¡ ±Í¹«°¡¼³ÀÌ ¿Ç´Ù´Â °á·ÐÀ» ³»¸®´Â °ÍÀº ³í¸®Àû ¿À·ùÀÌ´Ù. p°ª¿¡ ´ëÇÑ Áß´ëÇÑ ¿ÀÇØ°¡ ¸¹ÀÌ ÀÖÀ¸¸ç ¿¬±¸ÀÚ´Â ³í¹®À» ÀÛ¼ºÇÒ ¶§ p°ªÀÇ Çؼ®¿¡ ½ÅÁßÇØ¾ß ÇÑ´Ù. ±Í¹«°¡¼³°ËÁ¤À» º¸¿ÏÇϰųª ´ëüÇÒ ¼ö ÀÖ´Â ´ë¾ÈÀ¸·Î¼­, È¿°ú Å©±â, ½Å·Ú±¸°£, º£ÀÌÁö¾È Åë°è µîÀÌ ÀÖ´Ù.

There are many problems in evaluating study results by p value in null hypothesis testing for dental research. It is a logical fallacy to conclude that the null hypothesis is true when the it is not rejected. There are much serious misunderstanding about p value, and researchers should be cautious about interpreting p value in writing papers. As alternatives to complement or replace the null hypothesis significance testing, effect size, confidence interval, and Bayesian statistics are introduced.

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Null hypothesis; Significance testing; p value; Effect size; Confidence interval; Bayesian statistics

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