When is statistical significance not significant?

Filho, Dalson Britto Figueiredo; Paranhos, Ranulfo; Rocha, Enivaldo Carvalho da; Batista, Mariana; Júnior, José Alexandre da Silva; Santos, Manoel Leonardo; Marino, Jacira Guiro

doi:10.1590/s1981-38212013000100002

Cited by 85 publications

(50 citation statements)

References 22 publications

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“…LARS-WG performed better in capturing the multimodal peaks seen in observed temperatures and was able to reproduce the observed data for both weather variables (temperature and precipitation) better than the other two generators as observed from the density plots [62]. LARS-WG also had very good representation of the dry and wet day sequences, consistent with [76], and performed well at simulating snow days and precipitation events, making it suitable for hydrologic studies.…”

Section: Discussionmentioning

confidence: 80%

“…Initial analysis involved plotting the distributions and computing descriptive statistics for the daily precipitation and minimum and maximum air temperature values predicted by each weather generator and comparing the results with the same statistics obtained from the observed data [62]. In addition, the number of days with zero magnitude precipitation was also computed for each generator and for observed data.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, in climate studies, statistical significance does not always provide an adequate basis for decision-making; for example, a rise in temperature by two degrees Celsius may not be statistically significant but it can adversely affect the vegetation growth and lead to ecological imbalances, possibly due to habitat alterations and/or melting glaciers [82][83][84]. In this study, emphasis was placed on visualization and an alternative to significance testing (Cohen's-d value), which provided a more reliable means of analyzing the resulting datasets [39,41,62].…”

Section: Discussionmentioning

confidence: 99%

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Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation

Mehan

Guo

Gitau

et al. 2017

Climate

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Abstract:Climate is one of the single most important factors affecting watershed ecosystems and water resources. The effect of climate variability and change has been studied extensively in some places; in many places, however, assessments are hampered by limited availability of long-term continuous climate data. Weather generators provide a means of synthesizing long-term climate data that can then be used in natural resource assessments. Given their potential, there is the need to evaluate the performance of the generators; in this study, three commonly used weather generators-CLImate GENerator (CLIGEN), Long Ashton Research Station Weather Generator (LARS-WG), and Weather Generators (WeaGETS) were compared with regard to their ability to capture the essential statistical characteristics of observed data (distribution, occurrence of wet and dry spells, number of snow days, growing season temperatures, and growing degree days). The study was based on observed 1966-2015 weather station data from the Western Lake Erie Basin (WLEB), from which 50 different realizations were generated, each spanning 50 years. Both CLIGEN and LARS-WG performed fairly well with respect to representing the statistical characteristics of observed precipitation and minimum and maximum temperatures, although CLIGEN tended to overestimate values at the extremes. This generator also overestimated dry sequences by 18%-30% and snow-day counts by 12%-19% when considered over the entire WLEB. It (CLIGEN) was, however, well able to simulate parameters specific to crop growth such as growing degree days and had an added advantage over the other generators in that it simulates a larger number of weather variables. LARS-WG overestimated wet sequence counts across the basin by 15%-38%. In addition, the optimal growth period simulated by LARS-WG also exceeded that obtained from observed data by 16%-29% basin-wide. Preliminary results with WeaGETS indicated that additional evaluation is needed to better define its parameters. Results provided insights into the suitability of both CLIGEN and LARS-WG for use with water resource applications.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Comparative Study of Different Stochastic Weather Generators for Long-Term Climate Data Simulation

Mehan

Guo

Gitau

et al. 2017

Climate

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“…Indeed, by increasing the SS value, the significance of the relevant factor in the procedure also increases. 26,27 It is observed in Tables 4 and 5 that the interactions between factors A and C were statistically significant to the SNR and variance of each color channel. In addition, the interactions between factors A and B in green and blue color channels were significant to the SNR.…”

Section: Discussionmentioning

confidence: 96%

Optimizing the setup of a flatbed color scanner for color measurement using full factorial design

Hasanlou

Shams‐Nateri

Izadan

2019

Color Research & Application

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The application of flatbed color scanners for the measurement of color in various industries, especially textile industry, has received a great deal of attention. The initial setup of a scanner and the investigation of factors with a significant effect on scanner outputs are necessary to measure a reliable color using the scanner. In the present study, the setup of a flatbed color scanner was optimized using a full factorial design technique. Three factors, including the scanning position on the glass surface of the scanner (A), cutting size of an image in pixels (B), and bit depth (C), at different levels as input factors, and signal-to-noise ratio and variance of the scanned image as the responses were considered for the evaluation of the reliability of the scanner color measurement. The results of analyzing the factorial design indicate that each of the three factors played a significant role in the scanner setup for the color measurement so that factor A demonstrated the most significant impact on the responses. K E Y W O R D Scalibration, color, factorial design, optimization, scanner, signal-to-noise ratio

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“…Figueiredo Filho et al (2013). Methodologically, we use both observational and simulation data to defend our view on the proper use of the p-value statistic in empirical research.…”

mentioning

confidence: 99%