Common misconceptions about data analysis and statistics.
J Pharmacol Exp Ther
; 351(1): 200-5, 2014 Oct.
Article
em En
| MEDLINE
| ID: mdl-25204545
ABSTRACT
Ideally, any experienced investigator with the right tools should be able to reproduce a finding published in a peer-reviewed biomedical science journal. In fact, however, the reproducibility of a large percentage of published findings has been questioned. Undoubtedly, there are many reasons for this, but one reason may be that investigators fool themselves due to a poor understanding of statistical concepts. In particular, investigators often make these mistakes 1) P-hacking, which is when you reanalyze a data set in many different ways, or perhaps reanalyze with additional replicates, until you get the result you want; 2) overemphasis on P values rather than on the actual size of the observed effect; 3) overuse of statistical hypothesis testing, and being seduced by the word "significant"; and 4) over-reliance on standard errors, which are often misunderstood.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Bioestatística
Idioma:
En
Revista:
J Pharmacol Exp Ther
Ano de publicação:
2014
Tipo de documento:
Article