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Related Concept Videos

Ordinal Level of Measurement00:55

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
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The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
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All data are not created equal.

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    Scientific publishing faced challenges in 2009 due to data irregularities found in figures. Rigorous figure screening revealed fundamental data problems, prompting a review of experimental basics.

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    Area of Science:

    • Scientific publishing
    • Data integrity
    • Manuscript screening

    Background:

    • The 2009 publication year began with challenges impacting the journal's usual volume.
    • Four accepted articles were withdrawn due to issues discovered during the screening process.

    Discussion:

    • Ongoing screening of manuscript figures revealed significant irregularities.
    • These alterations in figures often indicated fundamental problems with the underlying data.
    • The prevalence of these issues across multiple papers necessitates a re-evaluation of experimental methodologies.

    Key Insights:

    • Figure screening is crucial for maintaining data integrity in scientific publications.
    • Irregularities in data presentation can mask serious flaws in research findings.
    • A critical review of experimental basics is required to address systemic issues.

    Outlook:

    • Future publications will emphasize enhanced scrutiny of data and figures.
    • This situation highlights the need for improved training in data handling and presentation.
    • The journal aims to uphold the highest standards of scientific rigor despite these setbacks.