THE t TEST: An Introduction
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The t distribution is a probability distribution similar to the Normal distribution. It is commonly used to test hypotheses involving numerical data. This paper provides an understanding of the t distribution and uses a musculo-skeletal example to illustrate its application.
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... To understand the impact of this curriculum we performed surveying. We also performed statistical analysis [41,42] ...
... Then, we computed the average percentage of marks obtained in these COs for different semesters and analyzed these data for t-tests to understand the significance of the newly proposed curriculum. The t-tests is a statistical method used to determine if there is a significant difference between the means of two groups while considering the null hypothesis that there is no difference between the means of the paired groups [42]. After calculating the t-value of each group of data, we can calculate the respective probabilities, called the p-value from the t-distribution. ...
... After calculating the t-value of each group of data, we can calculate the respective probabilities, called the p-value from the t-distribution. It is common practice to reject the hypothesis when the p-value is less than 0.05 and to retain it when the p-value is greater than 0.05 [42]. While conducting literature reviews, we observed that many articles have utilized t-tests to assess the significance of their approach while designing a new curriculum [29,31]. ...
This paper proposes a novel curriculum for the microprocessors and microcontrollers laboratory course. The proposed curriculum blends structured laboratory experiments with an open-ended project phase, addressing complex engineering problems and activities. Microprocessors and microcontrollers are ubiquitous in modern technology, driving applications across diverse fields. To prepare future engineers for Industry 4.0, effective educational approaches are crucial. The proposed lab enables students to perform hands-on experiments using advanced microprocessors and microcontrollers while leveraging their acquired knowledge by working in teams to tackle self-defined complex engineering problems that utilize these devices and sensors, often used in the industry. Furthermore, this curriculum fosters multidisciplinary learning and equips students with problem-solving skills that can be applied in real-world scenarios. With recent technological advancements, traditional microprocessors and microcontrollers curricula often fail to capture the complexity of real-world applications. This curriculum addresses this critical gap by incorporating insights from experts in both industry and academia. It trains students with the necessary skills and knowledge to thrive in this rapidly evolving technological landscape, preparing them for success upon graduation. The curriculum integrates project-based learning, where students define complex engineering problems for themselves. This approach actively engages students, fostering a deeper understanding and enhancing their learning capabilities. Statistical analysis shows that the proposed curriculum significantly improves student learning outcomes, particularly in their ability to formulate and solve complex engineering problems, as well as engage in complex engineering activities.
... To understand the impact of this curriculum we performed surveying. We also performed statistical analysis [41,42] ...
... Then, we computed the average percentage of marks obtained in these COs for different semesters and analyzed these data for t-tests to understand the significance of the newly proposed curriculum. The t-tests is a statistical method used to determine if there is a significant difference between the means of two groups while considering the null hypothesis that there is no difference between the means of the paired groups [42]. After calculating the t-value of each group of data, we can calculate the respective probabilities, called the p-value from the t-distribution. ...
... After calculating the t-value of each group of data, we can calculate the respective probabilities, called the p-value from the t-distribution. It is common practice to reject the hypothesis when the p-value is less than 0.05 and to retain it when the p-value is greater than 0.05 [42]. While conducting literature reviews, we observed that many articles have utilized t-tests to assess the significance of their approach while designing a new curriculum [29,31]. ...
This paper proposes a novel curriculum for the microprocessors and microcontrollers laboratory course. The proposed curriculum blends structured laboratory experiments with an open‐ended project phase, addressing complex engineering problems and activities. Microprocessors and microcontrollers are ubiquitous in modern technology, driving applications across diverse fields. To prepare future engineers for Industry 4.0, effective educational approaches are crucial. The proposed lab enables students to perform hands‐on experiments using advanced microprocessors and microcontrollers while leveraging their acquired knowledge by working in teams to tackle self‐defined complex engineering problems that utilize these devices and sensors, often used in the industry. Furthermore, this curriculum fosters multidisciplinary learning and equips students with problem‐solving skills that can be applied in real‐world scenarios. With recent technological advancements, traditional microprocessors and microcontrollers curricula often fail to capture the complexity of real‐world applications. This curriculum addresses this critical gap by incorporating insights from experts in both industry and academia. It trains students with the necessary skills and knowledge to thrive in this rapidly evolving technological landscape, preparing them for success upon graduation. The curriculum integrates project‐based learning, where students define complex engineering problems for themselves. This approach actively engages students, fostering a deeper understanding and enhancing their learning capabilities. Statistical analysis shows that the proposed curriculum significantly improves student learning outcomes, particularly in their ability to formulate and solve complex engineering problems, as well as engage in complex engineering activities.
... In cases where parametric testing methods are not applicable, non-parametric testing methods such as Wilcoxon matching signature level testing are commonly used for statistical testing. Therefore, this article introduces the basic principles and prerequisites of the two types of testing methods, and explains their correct usage methods [6][7][8][9][10] ...
... It does not consider the distribution type of the data, but directly compares it using the symbol, size order number, comprehensive judgment of the ranking, severity, or quality level of the sample data. The level data that is difficult to process by the parametric method can be analyzed by the non parametric method, so its application range is wide [2,6,9]. This article focuses on analyzing the Wilcoxon matching signature level testing method. ...
Bioassays play a crucial role in the research of new drugs and vaccines. The significance test for differences is the most commonly used statistical method in the analysis of biometric data. The methods for testing the significance of differences in paired samples can be broadly divided into two categories. The first category is that the data population follows a normal distribution, and the commonly used method is paired t-test; The second type requires the use of nonparametric testing methods for statistical testing when parameter testing methods are not applicable. Commonly used methods include Wilcoxon matched signature level testing. This article introduces the basic principles and prerequisites of two types of testing methods, and in establishing a detection method for trypsin residue, takes the significance of the difference between the results of three enzyme-linked immunosorbent assay (ELISA) tests at 36℃ and the results at 37℃ for two batches of raw materials as an example to explain the correct use of the two testing methods. Correctly applying significance testing can establish the conclusions of experiments or investigations on a more scientific and reliable basis, avoiding simplification and absolutization.
... Normally distributed continuous variables are presented as the means ± standard deviations (SDs), and the intergroup differences were determined via t tests [34]. All nonnormally distributed continuous variables are presented as medians [interquartile ranges (IQRs)], and the intergroup differences were determined via the rank-sum test [35]. ...
Carotid plaque radiomics-included models have increased the predictive capacity of cardiovascular risk, but the radiomic features of these models were inconsistent in previous studies. Lipids could be used to select the most important radiomic feature. A retrospective case‒control study was performed in 153 diabetic and 76 non-diabetic patients with carotid plaque detected by ultrasound. Cerebro-cardiovascular disease (CCD), comprising coronary heart disease (CHD) and stroke, was the primary outcome. Clinical variables and radiomic features of longitudinal carotid plaque images were collected. Principal component analyses were used to compare the power of radiomic and lipid features in discrimination between diabetes, CCD patients, and their opposites. Partial least square regression, logistic regression analyses, and least absolute shrinkage and selection operator (LASSO) regression were performed for high-risk radiomic features. The diagnostic capacity of the models was evaluated. PCA based on radiomics or lipids did not show good discrimination of diabetes, CCD, and their opposites. There were 6 overlapping radiomic features associated with lipid profiles, but only original_firstorder_Mean was negatively associated with diabetic stroke [adjusted OR = 0.468 (0.243–0.902), P = 0.023] and nondiabetic CHD [adjusted OR = 0.311 (0.123–0.783), P = 0.013]. The associations remained independent in the LASSO regression models (β=-0.032 for diabetic stroke, and − 0.026 for non-diabetic CHD). The diagnostic capacity of lipid-related radiomics for diabetic stroke (0.556 to 0.688) and non-diabetic CHD (0.690 to 0.783) was increased by the combination of these clinical variables. Carotid plaque radiomics is associated with lipids and stroke in diabetes, and quantitative features are useful for therapeutic guidance and cardiovascular risk evaluation in clinical use.
... The observed difference has a higher statistical significance when the p-value is smaller. Statistical significance is commonly defined as a p-value of 0.05 or less [39,40]. ...
Parametric design is one of the thriving contemporary architectural treatments that not only has an influence on the design of building envelopes but is capable of affecting the users physically and psychologically. The efficiency of the daylight resulting from the facade is greatly affected by the envelope’s perforation shapes, sizes, and distribution. This research builds upon a prior study which investigated the effect of Parametric Patterned Façade on daylight performance, quality, and visual comfort concluding with the choice of the highest-performing pattern for daylight metrics. The chosen pattern is the “Triangles Pattern” which was put in situ where the field experiment took place. The current study analyzed the effect of illuminance, and illuminance distribution resulting from the parametric patterned facade on architecture students’ mood and task performance in the setting of an architecture studio-based tutoring. Both quantitative and qualitative research methods were adopted; quantitative through the utilization of a Spatial Ability Test to assess students’ task performance, and qualitative through a self-reporting mood assessment, and subjective daylight perception questionnaire. The analysis was conducted through three types of tests to investigate the research hypothesis; statistical analysis of hypothesis (t-test), PANAS schedule for mood assessment, and comparative analysis of the subjective questionnaire between the base case condition and the parametric patterned façade condition. The results of paired sample study indicated that the students’ performance was enhanced after the exposure to the second condition (PPF condition) over the first (BC condition). Also, for the NA assessment, both PANAS assessment and t-test analysis results showed a difference in means between the two conditions indicating that there was an enhancement of the NA. Finally, a framework is proposed for the utilization of a Parametric Patterned façade in an educational setting. The implications of this experiment inform architects of the importance of daylighting techniques to enrich the educational environment, and how to establish a research methodology for the enhancement of daylight design using both technical and psychological approaches.
... A t Test is a parametric test that is used to compare the means of two groups (Kim, 2015). The reason is to verify whether there is a significant difference between the means of independent sample data (Ugoni and Walker, 2014). ...
The standard forward transformation for the direct conversion of curvilinear geodetic
coordinates (φ, γ, Η) to its associated Cartesian coordinates (E, N, Z) has become a major
challenge in most countries. This is due to the non-existence of the ellipsoidal height (h) in
the modelling of their local geodetic reference network. Numerous studies in the past and
recent years have suggested various mathematical techniques for predicting and estimating
local ellipsoidal heights. Primary data used for the studies comprises of topographic data
obtained from a survey in the Ghana urban water supply project in the Greater Kumasi
Metropolitan Area (GKMA).This study considered an empirical evaluation of soft computing
techniques such as Back Propagation Artificial Neural Network (BPANN), Generalized
Regression Neural Network (GRNN), Radial Basis Function Artificial Neural Network
(RBFANN) and conventional methods such as Polynomial Regression Model (PRM),
Autoregressive Integrated Moving Average (ARIMA) and Least Square Regression (LSR).
The motive is to apply and assess for the first time in our study area, the working efficiency
of the aforementioned techniques. Each model technique was assessed based on statistical
hypothesis (F, t) tests and performance criteria indices such as arithmetic mean error (AME),
arithmetic mean square error (AMSE), minimum and maximum error value, and arithmetic
standard deviation (ASD). The statistical analysis of the results revealed that, RBFANN,
GRNN, BPANN, LSR, ARIMA and PRM, successfully estimated the ellipsoidal heights for
the study area. However, the ANN models (RBFANN, BPANN, GRNN) outperforms the
conventional models (LSR, PRM, ARIMA) in terms of accuracy and precision in estimating
the local ellipsoidal heights. Also, statistical findings revealed that RBFANN produced more
reliable results compared with the other methods. The main conclusion drawn from this study
is that, the method of using soft computing is very much promising and can be adopted to
solve some of the major problems related to height issues in Ghana. This study seeks to
contribute to the existing knowledge on establishing a precise geodetic vertical datum in
Ghana for national heightening purpose.
... The results of the test are shown in Table 4. In t-test analysis, the effect of binary variables on the dependent variable is checked, and predictions are made, taking it criterion for the p-value to be lower than 0.05 (Ugoni & Walker, 1995). When the t-test results were analyzed, every factor was seen to have p values greater than 0.05. ...
The fact that travel agencies, one of the intermediaries of the distribution channel in the tourism sector, were seen as a
business line dating to the nineteenth century. The main aim of the study is to investigate whether there is a relationship
between the soft success factors (competence, reliability, empathy, communication, and responsiveness) of the type of
travel agency employees and product sales success. Another aim of the study is to determine whether these two factors
differ according to demographic variables. For this purpose, frequency, correlation, independent t-test, and one-way
variance is used in the analysis of the data. In addition, Structural Equation analysis is used to create a model because
of the correlation analysis. It is concluded that there is a positive and significant relationship between the soft success
factors of travel agency employees and their product sales success. It is determined that there is no significant difference
between the gender of the employees and the soft success factors and product sales success. It has been observed that
employees with soft success factors are more effective in sales success. No study has been found that addresses the
relationship between soft success factors and sales success in travel agencies, therefore it is thought to be an original
paper that will contribute to the literature in this regard.
Analysis of the research productivity for any university is so important in order to raise its international ranking. Rankings offer universities evidence that the education they deliver is of high quality and top standard. A student’s level of dedication to their studies directly affects the outcome of their academic career. Sitting in on a lecture at a top-five rated institution is far less significant than actively contributing (engaging with classmates, doing research, etc.) at a top-50 ranked university. Using a SciVal dataset of 13 university entities across the Kingdom of Saudi Arabia over a span of 5 years (2017–2021), we conducted a scientometric study for three categories, namely Output (O), Impact (I), and Collaboration (C), incorporating a total of 18 features. The methodology for selecting universities in this research depended on selecting the best universities in the Kingdom of Saudi Arabia in terms of the number of published research papers and the number of citations. This article aims to forecast the pattern of development and shortcomings faced by researchers from around the country from 2017 to 2021. The dataset is evaluated at the university level with homogenized features termed as “Scholar Plot” (SP), a popular approach to maintain and encourage development at the individual level. It is concluded that variances in efficiency within each knowledge field are the major drivers of heterogeneity in scientific output. Disparities in quality and specialization play a lesser impact in influencing productivity differences. The measure of such disparities using the mean of the group’s significance is illustrated using a t-tests statistical approach.
Probability allows us to infer from a sample to a population. In fact, inference is a tool of probability theory. This paper looks briefly at the Binomial, Poisson, and Normal distributions. These are probability distributions, which are used extensively in inference. An understanding of these distributions will assist the chiropractor and osteopath to critically appraise the literature.
A sound knowledge of basic statistics and its unique language has always been essential for those health professionals who perform and evaluate medical studies. In recent years this need has been magnified owing to the availability of relatively user-friendly statistical software packages, which allow an individual to perform a variety of statistical procedures with relative ease, even in the absence of a thorough knowledge of the underlying principles. Thus, never before has it been as important for those involved in the preparation or evaluation of written scientific reports to be so fully versed in the appropriate use and limitations of different statistical procedures. Despite the number of introductory texts in statistics, there are relatively few that present this subject in an uncomplicated, digestible manner and highlight the areas of importance for a given discipline. Basic and Clinical Biostatistics does just this for the application and appropriate use of statistics in
In this paper, the definition of a statistical hypothesis is discussed, and the considerations which need to be addressed when testing a hypothesis. In particular, the p-value, significance level, and power of a test are reviewed. Finally, the often quoted confidence interval is given a brief introduction.