"metabias" meaning in English

See metabias in All languages combined, or Wiktionary

Noun

Forms: metabiases [plural]
Etymology: meta- + bias Etymology templates: {{prefix|en|meta|bias}} meta- + bias Head templates: {{en-noun|es}} metabias (plural metabiases)
  1. A biasing factor, such as publication bias, that results in the available data becoming artificially skewed. Categories (topical): Biases Synonyms: meta-bias, meta bias Hyponyms: publication bias, reporting bias, selection bias Related terms: bias blindspot
    Sense id: en-metabias-en-noun-Q~GZGOV6 Categories (other): English entries with incorrect language header, English terms prefixed with meta-

Inflected forms

Alternative forms

Download JSON data for metabias meaning in English (4.3kB)

{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "meta",
        "3": "bias"
      },
      "expansion": "meta- + bias",
      "name": "prefix"
    }
  ],
  "etymology_text": "meta- + bias",
  "forms": [
    {
      "form": "metabiases",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {
        "1": "es"
      },
      "expansion": "metabias (plural metabiases)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [
            "Entries with incorrect language header",
            "Entry maintenance"
          ],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "English terms prefixed with meta-",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "topical",
          "langcode": "en",
          "name": "Biases",
          "orig": "en:Biases",
          "parents": [
            "Psychology",
            "Statistics",
            "Social sciences",
            "Formal sciences",
            "Mathematics",
            "Sciences",
            "Society",
            "All topics",
            "Fundamental"
          ],
          "source": "w"
        }
      ],
      "examples": [
        {
          "ref": "2010, John R. Woodward, Nottingham, “The Necessity of Meta Bias in Search Algorithms”, in International Conference on Computational Intelligence and Software Engineering (CiSE), number Dec., IEEE, →DOI, page 2b",
          "text": "An algorithm needs some method of altering its base bias as it is presented with sets of instances. The major claim of this paper is that, as the vast majority of algorithms are intended for use on a collection of problem instances, that meta bias is therefore necessary. Evidence for the fact that algorithms are intended to be reused is that the proponents of newly proposed algorithm test them on a number of problem instances which are reported in their papers. Meta bias is any mechanism which can adjust the base bias (see figure 2 i.e. it is a probability distribution over a set of base biases).",
          "type": "quotation"
        },
        {
          "ref": "2012, AJ Nordmann, B Kasenda, M Briel, “Meta-analyses: what they can and cannot do”, in Swiss medical weekly",
          "text": "One of the most important metabiases is reporting bias. Only about 50% of randomised trials ultimately reach publication in a journal indexed in a major electronic database",
          "type": "quotation"
        },
        {
          "ref": "2017, FR Schroeck, BL Jacobs, SB Bhayani, PL Nguyen, D Penson, J Hu, “Cost of New Technologies in Prostate Cancer Treatment: Systematic Review of Costs and Cost Effectiveness of Robotic-assisted Laparoscopic Prostatectomy, Intensity-modulated Radiotherapy, and Proton Beam Therapy”, in European urology, Elsevier",
          "text": "Given the topic of this review, formal assessment of metabiases such as publication bias or selective reporting within studies was not feasible.",
          "type": "quotation"
        },
        {
          "ref": "2020 March 19, Derek S. Chew, Eric Black-Maier, Zak Loring, Peter A. Noseworthy, Douglas L. Packer, Derek V. Exner, Daniel B. Mark, Jonathan P. Piccini, “Diagnosis-to-Ablation Time and Recurrence of Atrial Fibrillation Following Catheter Ablation. A Systematic Review and Meta-Analysis of Observational Studies”, in Arrhythmia and Electrophysiology, number 13:e008128, →DOI",
          "text": "Heterogeneity across the studies was tested with the Cochran Q and I2 statistics. We considered an I2 statistic of >25% as a low degree of heterogeneity, >50% as moderate heterogeneity, and >75% as high heterogeneity. To ascertain potential meta-bias, a funnel plot was qualitatively assessed for asymmetry.",
          "type": "quotation"
        },
        {
          "ref": "2021, Desai S, Mehta KM, Singh RJ, et al., “Effects of integrated economic and health interventions with women’s groups on health-related knowledge, behaviours and outcomes in low-income and middle-income countries: a systematic review protocol.”, in BMJ Open",
          "text": "Metabiases / We will plot study sample size against effect size to evaluate whether these are skewed and asymmetrical in the presence of publication bias and other biases. In addition, the risk of bias assessment tools will identify whether studies indicate selective reporting of outcomes, which we will summarise across studies if possible.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "A biasing factor, such as publication bias, that results in the available data becoming artificially skewed."
      ],
      "hyponyms": [
        {
          "word": "publication bias"
        },
        {
          "word": "reporting bias"
        },
        {
          "word": "selection bias"
        }
      ],
      "id": "en-metabias-en-noun-Q~GZGOV6",
      "related": [
        {
          "word": "bias blindspot"
        }
      ],
      "synonyms": [
        {
          "word": "meta-bias"
        },
        {
          "word": "meta bias"
        }
      ]
    }
  ],
  "word": "metabias"
}
{
  "etymology_templates": [
    {
      "args": {
        "1": "en",
        "2": "meta",
        "3": "bias"
      },
      "expansion": "meta- + bias",
      "name": "prefix"
    }
  ],
  "etymology_text": "meta- + bias",
  "forms": [
    {
      "form": "metabiases",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {
        "1": "es"
      },
      "expansion": "metabias (plural metabiases)",
      "name": "en-noun"
    }
  ],
  "hyponyms": [
    {
      "word": "publication bias"
    },
    {
      "word": "reporting bias"
    },
    {
      "word": "selection bias"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "related": [
    {
      "word": "bias blindspot"
    }
  ],
  "senses": [
    {
      "categories": [
        "English countable nouns",
        "English entries with incorrect language header",
        "English lemmas",
        "English nouns",
        "English terms prefixed with meta-",
        "English terms with quotations",
        "en:Biases"
      ],
      "examples": [
        {
          "ref": "2010, John R. Woodward, Nottingham, “The Necessity of Meta Bias in Search Algorithms”, in International Conference on Computational Intelligence and Software Engineering (CiSE), number Dec., IEEE, →DOI, page 2b",
          "text": "An algorithm needs some method of altering its base bias as it is presented with sets of instances. The major claim of this paper is that, as the vast majority of algorithms are intended for use on a collection of problem instances, that meta bias is therefore necessary. Evidence for the fact that algorithms are intended to be reused is that the proponents of newly proposed algorithm test them on a number of problem instances which are reported in their papers. Meta bias is any mechanism which can adjust the base bias (see figure 2 i.e. it is a probability distribution over a set of base biases).",
          "type": "quotation"
        },
        {
          "ref": "2012, AJ Nordmann, B Kasenda, M Briel, “Meta-analyses: what they can and cannot do”, in Swiss medical weekly",
          "text": "One of the most important metabiases is reporting bias. Only about 50% of randomised trials ultimately reach publication in a journal indexed in a major electronic database",
          "type": "quotation"
        },
        {
          "ref": "2017, FR Schroeck, BL Jacobs, SB Bhayani, PL Nguyen, D Penson, J Hu, “Cost of New Technologies in Prostate Cancer Treatment: Systematic Review of Costs and Cost Effectiveness of Robotic-assisted Laparoscopic Prostatectomy, Intensity-modulated Radiotherapy, and Proton Beam Therapy”, in European urology, Elsevier",
          "text": "Given the topic of this review, formal assessment of metabiases such as publication bias or selective reporting within studies was not feasible.",
          "type": "quotation"
        },
        {
          "ref": "2020 March 19, Derek S. Chew, Eric Black-Maier, Zak Loring, Peter A. Noseworthy, Douglas L. Packer, Derek V. Exner, Daniel B. Mark, Jonathan P. Piccini, “Diagnosis-to-Ablation Time and Recurrence of Atrial Fibrillation Following Catheter Ablation. A Systematic Review and Meta-Analysis of Observational Studies”, in Arrhythmia and Electrophysiology, number 13:e008128, →DOI",
          "text": "Heterogeneity across the studies was tested with the Cochran Q and I2 statistics. We considered an I2 statistic of >25% as a low degree of heterogeneity, >50% as moderate heterogeneity, and >75% as high heterogeneity. To ascertain potential meta-bias, a funnel plot was qualitatively assessed for asymmetry.",
          "type": "quotation"
        },
        {
          "ref": "2021, Desai S, Mehta KM, Singh RJ, et al., “Effects of integrated economic and health interventions with women’s groups on health-related knowledge, behaviours and outcomes in low-income and middle-income countries: a systematic review protocol.”, in BMJ Open",
          "text": "Metabiases / We will plot study sample size against effect size to evaluate whether these are skewed and asymmetrical in the presence of publication bias and other biases. In addition, the risk of bias assessment tools will identify whether studies indicate selective reporting of outcomes, which we will summarise across studies if possible.",
          "type": "quotation"
        }
      ],
      "glosses": [
        "A biasing factor, such as publication bias, that results in the available data becoming artificially skewed."
      ]
    }
  ],
  "synonyms": [
    {
      "word": "meta-bias"
    },
    {
      "word": "meta bias"
    }
  ],
  "word": "metabias"
}

This page is a part of the kaikki.org machine-readable English dictionary. This dictionary is based on structured data extracted on 2024-05-18 from the enwiktionary dump dated 2024-05-02 using wiktextract (1d5a7d1 and 304864d). The data shown on this site has been post-processed and various details (e.g., extra categories) removed, some information disambiguated, and additional data merged from other sources. See the raw data download page for the unprocessed wiktextract data.

If you use this data in academic research, please cite Tatu Ylonen: Wiktextract: Wiktionary as Machine-Readable Structured Data, Proceedings of the 13th Conference on Language Resources and Evaluation (LREC), pp. 1317-1325, Marseille, 20-25 June 2022. Linking to the relevant page(s) under https://kaikki.org would also be greatly appreciated.