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PubMed1991

Improper statistics.

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Data Interpretation, Statistical↗
PubMedDate not supplied

Using statistics.

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Data Interpretation, Statistical↗
PubMed1985

Aboriginal socio-economic characteristics: issues affecting the interpretation of statistics.

Statistical data on aborigines in Australia are examined, and it is recognized that internationally accepted census definitions and criteria may not be appropriate for analyzing the situation of aboriginal groups. In particular, the author "uses 1981 census-based comparisons of some Aboriginal and non-Aboriginal socio-economic characteristics and discusses how these assessments are affected by Aboriginal attributes. Attention is confined to three commonly used socio-economic indicators: labour force status, occupational category and income (on an individual and per capita basis)--all examined on an Australia-wide basis and specifically for the Northern Territory."

Australia↗
PubMed1989

The box plot: a simple visual method to interpret data.

Exploratory data analysis involves the use of statistical techniques to identify patterns that may be hidden in a group of numbers. One of these techniques is the "box plot," which is used to visually summarize and compare groups of data. The box plot uses the median, the approximate quartiles, and the lowest and highest data points to convey the level, spread, and symmetry of a distribution of data values. It can also be easily refined to identify outlier data values and can be easily constructed by hand. We apply box plots to tabular data from two recently published articles to show how readers can use box plots to improve the interpretation of data in complex tables. The box plot, like other visual methods, is more than a substitute for a table: It is a tool that can improve our reasoning about quantitative information. We recommend that the box plot be used more frequently.

Alcohol Drinking↗
PubMed1994

The statistical analysis of single-subject data: a comparative examination.

BACKGROUND AND PURPOSE: The purposes of this study were to examine whether the use of three different statistical methods for analyzing single-subject data led to similar results and to identify components of graphed data that influence agreement (or disagreement) among the statistical procedures. METHODS: Forty-two graphs containing single-subject data were examined. Twenty-one were AB charts of hypothetical data. The other 21 graphs appeared in Journal of Applied Behavioral Analysis, Physical Therapy, Journal of the Association for Persons With Severe Handicaps, and Journal of Behavior Therapy and Experimental Psychiatry. Three different statistical tests--the C statistic, the two-standard deviation band method, and the split-middle method of trend estimation--were used to analyze the 42 graphs. RESULTS: A relatively low degree of agreement (38%) was found among the three statistical tests. The highest rate of agreement for any two statistical procedures (71%) was found for the two-standard deviation band method and the C statistic. A logistic regression analysis revealed that overlap in single-subject graphed data was the best predictor of disagreement among the three statistical tests (beta = .49, P < .03). CONCLUSION AND DISCUSSION: The results indicate that interpretation of data from single-subject research designs is directly influenced by the method of data analysis selected. Variation exists across both visual and statistical methods of data reduction. The advantages and disadvantages of statistical and visual analysis are described.

Data Interpretation, Statistical↗
PubMed1991

Interpretation of interaction in factorial analysis of variance design.

The validity of statistical conclusions in medical research depends on proper analysis and interpretation of collected data. One potential area of invalidity is the inappropriate post hoc analysis of statistically significant interactions in the analysis of variance of factorial designs. This paper examines the statistical explanations included in 83 studies published in three leading medical journals where the findings indicated significant interaction effects. Only 24 per cent of the reported statistically significant interactions had an accompanying correct interpretation. The most common form of misinterpretation involved a comparison of individual cell means within a row or column of one factor used in the design. This interpretation did not conform to the factorial ANOVA model with interaction. This misinterpretation occurs when the correct omnibus test of a hypothesis is followed by an incorrect post hoc analysis and/or an inaccurate assessment of the original statistical result.

Analysis of Variance↗
PubMed1980

Interpretation of research data: selected statistical procedures.

Selected statistical procedures used in the analysis of research data are presented. The relationship of significance testing to research hypotheses is explained in terms of tests of differences and correlation. Also, the differences, assumptions, and advantages and disadvantages of parametric and nonparametric statistics are discussed. With regard to each statistic presented, emphasis is placed on the hypotheses that would be tested, the kinds of data for which the statistic is appropriate, the method of calculation, and how to test for "significance." The selected statistical procedures include the Student's t-test and chi square. An explanation of the concept of correlation is provided, and several correlation coefficients are discussed, including the Pearson r, Spearman rho, Kendall's tau, the point biserial, biserial, phi coefficient, and contingency coefficient. Pharmacists must know basic statistical procedures in order to be able to effectively interpret the results of published research or to appropriately analyze data that have been collected in their own research endeavors.

Methods↗
PubMed1996

Technology assessment in critical care: understanding statistical analyses used to assess agreement between methods of clinical measurement.

Many new measurement methods that employ various technologies to measure physiological parameters have been introduced into the field of critical care. Clinical assessment of these new methods occurs through the conduct of method-comparison studies in which the level of agreement between a new measurement method and a clinical standard method is determined. Clinicians and researchers are often faced with the complicated task of analyzing and interpreting the results of method-comparison studies. Use of correlation and linear regression techniques has been prevalent in method-comparison studies but has proven inappropriate and inadequate in determining how well methods compare. The purposes of this article are to briefly review the terms of accuracy, agreement, and the precision in context with method-comparison studies, and discuss inappropriate and appropriate statistical analyses and their interpretation. Appropriate data analysis of method-comparison studies will aid in determining not only whether new monitoring methods can be interchanged or used in place of existing methods, but whether new methods warrant further research of their effect on patient outcomes.

Critical Care↗
PubMed2000

Vocational students' learning preferences: the interpretability of ipsative data.

A number of researchers have argued that ipsative data are not suitable for statistical procedures designed for normative data. Others have argued that the interpretability of such analyses of ipsative data are little affected where the number of variables and the sample size are sufficiently large. The research reported here represents a factor analysis of the scores on the Canfield Learning Styles Inventory for 1,252 students in vocational education. The results of the factor analysis of these ipsative data were examined in a context of existing theory and research on vocational students and lend support to the argument that the factor analysis of ipsative data can provide sensibly interpretable results.

Adolescent↗
PubMed1994

Statistical significance of amino acid sequence similarity in type II DNA methyltransferases.

The statistical significance of amino acid sequence similarities previously observed in type II DNA methyltransferases has been investigated. It is shown: (1) that the intramolecular similarities observed among various type II Mtases are not statistically significant and thus can not be used to support a gene duplication model; (2) that the intermolecular similarities observed in a peptide in various type II adenine methylases are statistically confirmed; (3) that the similarities observed between MutH and these proteins for this peptide are not statistically significant and therefore cannot be used to propose a functional role in DNA recognition for this peptide.

Amino Acid Sequence↗
PubMed1991

Mental retardation and WAIS-R difference scores.

This article explores the psychometric properties (reliabilities, standard deviations and measurement errors) of Wechsler Adult Intelligence Scale--Revised (Wechsler, 1981) subtest difference scores. The sample consisted of 290 subjects with IQ less than 80. Results demonstrated less than satisfactory difference score reliability and disproportionate measurement error. Nevertheless, neither property was so inadequate as to render cautious profile interpretation impossible. The tabled values can help clinicians working with developmentally delayed clients interpret differences between subtest scores based on statistically reliable discrepancies.

Data Interpretation, Statistical↗
PubMed2004

Fundamentals of massive automatic pairwise alignments of protein sequences: theoretical significance of Z-value statistics.

MOTIVATION: Different automatic methods of sequence alignments are routinely used as a starting point for homology searches and function inference. Confidence in an alignment probability is one of the major fundamentals of massive automatic genome-scale pairwise comparisons, for clustering of putative orthologs and paralogs, sequenced genome annotation or multiple-genomic tree constructions. Extreme value distribution based on the Karlin-Altschul model, usually advised for large-scale comparisons are not always valid, particularly in the case of comparisons of non-biased with nucleotide-biased genomes (such that of Plasmodium falciparum). Z-values estimates based on Monte Carlo technics, can be calculated experimentally for any alignment output, whatever the method used. Empirically, a Z-value higher than approximately 8 is supposed reasonable to assess that an alignment score is significant, but this arbitrary figure was never theoretically justified. RESULTS: In this paper, we used the Bienaymé-Chebyshev inequality to demonstrate a theorem of the upper limit of an alignment score probability (or P-value). This theorem implies that a computed Z-value is a statistical test, a single-linkage clustering criterion and that 1/Z-value(2) is an upper limit to the probability of an alignment score whatever the actual probability law is. Therefore, this study provides the missing theoretical link between a Z-value cut-off used for an automatic clustering of putative orthologs and/or paralogs, and the corresponding statistical risk in such genome-scale comparisons (using non-biased or biased genomes).

Algorithms↗
PubMed1998

Independence and statistical inference in clinical trial designs: a tutorial review.

The requirements for statistical approaches to the design, analysis, and interpretation of experimental data are now accepted by the scientific community. This is of particular importance in medical studies where public health consequences are of concern. Investigators in the clinical sciences should be cognizant of statistical principles in general, but should always be wary of the pursuing their own analyses and engage statisticians for data analysis whenever possible. Examples of circumstances that require statistical evaluation not found in textbooks and not always obvious to the lay person are pervasive. Incorrect statistical evaluation and analyses in such situations will result in erroneous and potentially serious misleading interpretation of clinical data. Although a statistician may not be responsible for any misinterpretations in such unfortunate circumstances, the quote often cited about statisticians and "damned liars" may appear to be more truth than fable. This article is a tutorial review and describes a common misuse of clinical data resulting in an apparently large sample size derived from a small number of patients. This mistake is a consequence of ignoring the dependency of results, treating multiple observations from a single patient as independent observations.

Data Interpretation, Statistical↗