Quantifying annual patterns in the frequency of mammalian births: do goodness-of-fit tests provide adequate inferences?

2012 ◽  
Vol 60 (6) ◽  
pp. 381 ◽  
Author(s):  
Evan Watkins ◽  
Julian Di Stefano
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Goodness Of Fit ◽  
A Priori ◽  
Simulated Data ◽  
Maximum Effect ◽  
Chi Square ◽  
Goodness Of Fit Tests ◽  
Power Studies

Hypotheses relating to the annual frequency distribution of mammalian births are commonly tested using a goodness-of-fit procedure. Several interacting factors influence the statistical power of these tests, but no power studies have been conducted using scenarios derived from biological hypotheses. Corresponding to theories relating reproductive output to seasonal resource fluctuation, we simulated data reflecting a winter reduction in birth frequency to test the effect of four factors (sample size, maximum effect size, the temporal pattern of response and the number of categories used for analysis) on the power of three goodness-of-fit procedures – the G and Chi-square tests and Watson’s U2 test. Analyses resulting in high power all had a large maximum effect size (60%) and were associated with a sample size of 200 on most occasions. The G-test was the most powerful when data were analysed using two temporal categories (winter and other) while Watson’s U2 test achieved the highest power when 12 monthly categories were used. Overall, the power of most modelled scenarios was low. Consequently, we recommend using power analysis as a research planning tool, and have provided a spreadsheet enabling a priori power calculations for the three tests considered.

scholarly journals A Multi-faceted Mess: A Review of Statistical Power Analysis in Psychology Journal Articles

2019 ◽  
Author(s):  
Rob Cribbie ◽  
Nataly Beribisky ◽  
Udi Alter
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
A Priori ◽  
Type I ◽  
Specific Level ◽  
Maximum Sample Size ◽  
Power Analyses

Many bodies recommend that a sample planning procedure, such as traditional NHST a priori power analysis, is conducted during the planning stages of a study. Power analysis allows the researcher to estimate how many participants are required in order to detect a minimally meaningful effect size at a specific level of power and Type I error rate. However, there are several drawbacks to the procedure that render it “a mess.” Specifically, the identification of the minimally meaningful effect size is often difficult but unavoidable for conducting the procedure properly, the procedure is not precision oriented, and does not guide the researcher to collect as many participants as feasibly possible. In this study, we explore how these three theoretical issues are reflected in applied psychological research in order to better understand whether these issues are concerns in practice. To investigate how power analysis is currently used, this study reviewed the reporting of 443 power analyses in high impact psychology journals in 2016 and 2017. It was found that researchers rarely use the minimally meaningful effect size as a rationale for the chosen effect in a power analysis. Further, precision-based approaches and collecting the maximum sample size feasible are almost never used in tandem with power analyses. In light of these findings, we offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as collecting the maximum sample size feasible.

scholarly journals Causality in Statistical Power: Isomorphic Properties of Measurement, Research Design, Effect Size, and Sample Size

Scientifica ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
R. Eric Heidel
Keyword(s):  
Sample Size ◽  
Cognitive Dissonance ◽  
Research Design ◽  
Effect Size ◽  
Statistical Power ◽  
Research Study ◽  
A Priori ◽  
Sample Size Calculation ◽  
Design Effect

Statistical power is the ability to detect a significant effect, given that the effect actually exists in a population. Like most statistical concepts, statistical power tends to induce cognitive dissonance in hepatology researchers. However, planning for statistical power by ana priorisample size calculation is of paramount importance when designing a research study. There are five specific empirical components that make up ana priorisample size calculation: the scale of measurement of the outcome, the research design, the magnitude of the effect size, the variance of the effect size, and the sample size. A framework grounded in the phenomenon of isomorphism, or interdependencies amongst different constructs with similar forms, will be presented to understand the isomorphic effects of decisions made on each of the five aforementioned components of statistical power.

scholarly journals AN ALTERNATIVE TO CLASSICAL LATENT CLASS MODELS SELECTION METHODS FOR SPARSE BINARY DATA: AN ILLUSTRATION WITH SIMULATED DATA

2017 ◽  
Vol 23 (1) ◽  
pp. 199-220
Author(s):  
Carlomagno Araya Alpizar
Keyword(s):  
Binary Data ◽  
Goodness Of Fit ◽  
Latent Class ◽  
Empirical Distribution ◽  
Latent Class Model ◽  
Simulated Data ◽  
Latent Class Models ◽  
Type I ◽  
Chi Square ◽  
Goodness Of Fit Tests

Within the context of a latent class model with manifest binary variables, we propose an alternative method that solves the problem of estimating empirical distribution with sparse contingency tables and the chi-square approximation for goodness-of-fit will not be valid. We analyze sparse binary data, where there are many response patterns with very small expected frequencies in several data sets varying in degree of sparseness from 1 to 5 defined d = n/2p = n/R is a factor that is mentioned in almost all prior literature as being an important determinant of how well the distribution is represented by the chi-squared.The proposed approach produced results that were valid and reliable under the mentioned problematic data conditions. Results from the proposal presented compare the rates of Type I for traditional goodness-of-fit tests. We also show that with data density d ≤ 5, Pearson’s statistic

scholarly journals Statistical Primer for Athletic Trainers: Understanding the Role of Statistical Power in Comparative Athletic Training Research

2018 ◽  
Vol 53 (7) ◽  
pp. 716-719
Author(s):  
Monica R. Lininger ◽  
Bryan L. Riemann
Keyword(s):  
Sample Size ◽  
Athletic Training ◽  
Treatment Effect ◽  
Statistical Power ◽  
Athletic Trainers ◽  
A Priori ◽  
Significant Treatment ◽  
Training Research ◽  
Α Level

Objective: To describe the concept of statistical power as related to comparative interventions and how various factors, including sample size, affect statistical power.Background: Having a sufficiently sized sample for a study is necessary for an investigation to demonstrate that an effective treatment is statistically superior. Many researchers fail to conduct and report a priori sample-size estimates, which then makes it difficult to interpret nonsignificant results and causes the clinician to question the planning of the research design.Description: Statistical power is the probability of statistically detecting a treatment effect when one truly exists. The α level, a measure of differences between groups, the variability of the data, and the sample size all affect statistical power.Recommendations: Authors should conduct and provide the results of a priori sample-size estimations in the literature. This will assist clinicians in determining whether the lack of a statistically significant treatment effect is due to an underpowered study or to a treatment's actually having no effect.

scholarly journals Statistical Power in Content Analysis Designs: How Effect Size, Sample Size and Coding Accuracy Jointly Affect Hypothesis Testing ‐ A Monte Carlo Simulation Approach.

2021 ◽  
Vol 3 (1) ◽  
pp. 61-89
Author(s):  
Stefan Geiß
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Content Analysis ◽  
Sample Size ◽  
Effect Size ◽  
Statistical Power ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Expected Effect ◽  
Sample Size Effect

Abstract This study uses Monte Carlo simulation techniques to estimate the minimum required levels of intercoder reliability in content analysis data for testing correlational hypotheses, depending on sample size, effect size and coder behavior under uncertainty. The ensuing procedure is analogous to power calculations for experimental designs. In most widespread sample size/effect size settings, the rule-of-thumb that chance-adjusted agreement should be ≥.80 or ≥.667 corresponds to the simulation results, resulting in acceptable α and β error rates. However, this simulation allows making precise power calculations that can consider the specifics of each study’s context, moving beyond one-size-fits-all recommendations. Studies with low sample sizes and/or low expected effect sizes may need coder agreement above .800 to test a hypothesis with sufficient statistical power. In studies with high sample sizes and/or high expected effect sizes, coder agreement below .667 may suffice. Such calculations can help in both evaluating and in designing studies. Particularly in pre-registered research, higher sample sizes may be used to compensate for low expected effect sizes and/or borderline coding reliability (e.g. when constructs are hard to measure). I supply equations, easy-to-use tables and R functions to facilitate use of this framework, along with example code as online appendix.

scholarly journals Estimating the Best-Fitted Probability Distribution for Monthly Maximum Temperature at the Sylhet Station in Bangladesh

2021 ◽  
Vol 2 (2) ◽  
pp. 60-67
Author(s):  
Rashidul Hasan Rashidul Hasan
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Probability Distributions ◽  
Probability Model ◽  
Temperature Data ◽  
Maximum Temperature ◽  
Chi Square ◽  
Goodness Of Fit Tests ◽  
Anderson Darling ◽  
Log Normal

The estimation of a suitable probability model depends mainly on the features of available temperature data at a particular place. As a result, existing probability distributions must be evaluated to establish an appropriate probability model that can deliver precise temperature estimation. The study intended to estimate the best-fitted probability model for the monthly maximum temperature at the Sylhet station in Bangladesh from January 2002 to December 2012 using several statistical analyses. Ten continuous probability distributions such as Exponential, Gamma, Log-Gamma, Beta, Normal, Log-Normal, Erlang, Power Function, Rayleigh, and Weibull distributions were fitted for these tasks using the maximum likelihood technique. To determine the model’s fit to the temperature data, several goodness-of-fit tests were applied, including the Kolmogorov-Smirnov test, Anderson-Darling test, and Chi-square test. The Beta distribution is found to be the best-fitted probability distribution based on the largest overall score derived from three specified goodness-of-fit tests for the monthly maximum temperature data at the Sylhet station.

Inheritance and linkage of isozymes in Populus tremuloides (Michx.)

Genome ◽  
1987 ◽  
Vol 29 (2) ◽  
pp. 384-388 ◽  
Author(s):  
Jung O. Hyun ◽  
Om P. Rajora ◽  
Louis Zsuffa
Keyword(s):  
Populus Tremuloides ◽  
Goodness Of Fit ◽  
Single Gene ◽  
Gene Control ◽  
Glutamate Oxaloacetate Transaminase ◽  
Chi Square ◽  
Banding Patterns ◽  
Phosphogluconate Dehydrogenase ◽  
Goodness Of Fit Tests ◽  
Populus Species

Progenies of four controlled crosses were assayed electrophoretically to determine the inheritance of isozymes of 10 loci coding for six enzymes, aconitase (ACO), glutamate oxaloacetate transaminase (GOT), isocitrate dehydrogenase (IDH), phosphoglucomutase (PGM), 6-phosphogluconate dehydrogenase (6-PGD), and phosphoglucose isomerase (PGI), in roots of Populus tremuloides. Chi-square goodness of fit tests verified a single-gene Mendelian control of the segregating allozyme variants at each of five loci: Aco-1, Got, Pgm-2, 6-Pgd-2, and Pgi-2. Evidence was also obtained for a single-gene control of each of the remaining five loci (Aco-2, Idh, Pgm-1, 6-Pgd-1, and Pgi-1). ACO and PGM showed monomeric, while GOT, IDH, 6-PGD, and PGI had dimeric, banding patterns. The results of joint two-locus segregation tests indicated no linkage between 6-Pgd-2 and Pgi-2. Key words: Populus species, electrophoresis, allozymes, inheritance, linkage.

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