The Place of Statistics in Psychology

Nunnally, Jum C.

doi:10.1177/001316446002000401

Cited by 168 publications

(108 citation statements)

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“…For example, Bakan (1966;see also Berkson, 1938;Nunnally, 1960;Meehl, 1967) subdivided the data of 60,000 persons according to completely arbitrary criteria, like living east or west of the Mississippi river, living in the north or south of the USA, etc. and found all tests coming up statistically significant.…”

Section: Nhst Is Unsuitable For Large Datasetsmentioning

confidence: 99%

“…Critique of NHST started not much later (Jeffreys, 1939(Jeffreys, , 1948(Jeffreys, , 1961 and has been forcefully present since then (Jeffreys, 1939(Jeffreys, , 1948(Jeffreys, , 1961Eysenck, 1960;Nunnally, 1960;Rozeboom, 1960;Clark, 1963;Bakan, 1966;Meehl, 1967;Lykken, 1968) and continues to-date (Wasserstein and Lazar, 2016). The problems are numerous, and as Edwards (1972, p. 179) concluded 44 years ago: "any method which invites the contemplation of a null hypothesis is open to grave misuse, or even abuse."…”

Section: The State Of the Art Must Change Nhst Is Unsuitable As The Cmentioning

confidence: 99%

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When null hypothesis significance testing is unsuitable for research: a reassessment

Szűcs

Ioannidis

2016

Preprint

107

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Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of (cognitive) neuroscience, psychology, and biomedical science in general. We review these shortcomings and suggest that, after sustained negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. If theoretical predictions are weak we should not rely on all or nothing hypothesis tests. Different inferential methods may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Hypothesis-testing studies should be pre-registered and optimally raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out.

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Section: Nhst Is Unsuitable For Large Datasetsmentioning

confidence: 99%

Section: The State Of the Art Must Change Nhst Is Unsuitable As The Cmentioning

confidence: 99%

When null hypothesis significance testing is unsuitable for research: a reassessment

Szűcs

Ioannidis

2016

Preprint

107

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“…Alternatively, the investigator might have collected data until the difference between the numbers of males and females was 7, or until the difference was significant at some level. Each set of more extreme outcomes has its own probability, which, along with the probability of the result actually obtained, constitutes P The point is that determining which outcomes of an experiment or survey are more extreme than the observed one, so a P-value can be calculated, requires knowledge of the intentions of the investigator (Berger and Berry 1988 (Nunnally 1960).…”

Section: What Are More Extreme Data?mentioning

confidence: 99%

The Insignificance of Statistical Significance Testing

Johnson¹

1999

The Journal of Wildlife Management

932

602

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“…Let explain from the point of view of the theory of Haar null sets in R N some confusions which were described by Jum Nunnally [14] and Jacob Cohen [4]: Let x 1 , x 2 , · · · be an infinite sample obtained by observation on independent and normally distributed real-valued random variables with parameters (θ, 1), where θ is an unknown mean and the variance is equal to 1. Using this infinite sample we want to estimate an unknown mean.…”

Section: Introductionmentioning

confidence: 99%

Objective and Strong Objective Consistent Estimates of Unknown Parameters for Statistical Structures in a Polish Group Admitting an Invariant Metric

Kintsurashvili

Kiria²,

Pantsulaia

2016

Applications of Measure Theory to Statistics

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By using the notion of a Haar ambivalent set introduced by Balka, Buczolich and Elekes (2012), essentially new classes of statistical structures having objective and strong objective estimates of unknown parameters are introduced in a Polish non-locally-compact group admitting an invariant metric and relations between them are studied in this paper. An example of such a weakly separated statistical structure is constructed for which a question asking "whether there exists a consistent estimate of an unknown parameter" is not solvable within the theory (ZF ) & (DC). A question asking "whether there exists an objective consistent estimate of an unknown parameter for any statistical structure in a non-locally compact Polish group with an invariant metric when subjective one exists" is answered positively when there exists at least one such a parameter the pre-image of which under this subjective estimate is a prevalent. These results extend recent results of authors. Some examples of objective and strong objective consistent estimates in a compact Polish group {0; 1} N are considered in this paper.Primary 62-02; secondary 62D05.

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The Place of Statistics in Psychology

Cited by 168 publications

References 1 publication

When null hypothesis significance testing is unsuitable for research: a reassessment

When null hypothesis significance testing is unsuitable for research: a reassessment

The Insignificance of Statistical Significance Testing

Objective and Strong Objective Consistent Estimates of Unknown Parameters for Statistical Structures in a Polish Group Admitting an Invariant Metric

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