Hypothesis Testing. 4.6.1. Insurance productivity and the growth of Nigerian economy From the long-run coefficients displayed on Table 4, the proxy for insurance sector development and productivity (RGDPIS) was found to be a stimulant of Nigerian economic growth and it maintained positive and significant relationship with Nigerian economic growth, in a manner that increase in the insurance productivity would promote Nigerian economic growth significantly. Hence, since the probability value 0.0250<0.05, there insufficient evidence to accept the null hypothesis; hence the null hypothesis H01 which states that insurance sector productivity has no effect that is significant on the growth of Nigerian economy was accepted.
Hypothesis Testing. Based on the results of hypothesis testing, a summary table of the results of the research hypothesis is displayed as shown in Table 4. Table 4 Hypothesis testing results Competency Model of SME Entrepreneurs in Fashion (n = 129) Estimation Coefficient R2 F-count * & t-count Conclusion Individual Factors and External Factors → Cultural Orientation 0.369 36.842* Significant Significant 4.468 0.370 0.320 3.724 Significant Individual Factors and External Factors → Cross CulturalCompetence Individual Factors and External Factors → EntrepreneurialCompetence -0.061 0.791 Not Significant 0.387 39.773* Significant 0.420 5.906 Significant 0.174 2.493 Significant 0.413 44.325* Significant 0.382 3.855 Significant 0.348 3.043 Significant 0.223 18.081* Significant 0.379 5.428 Significant s → Cultural Orientation and Cross-Cultural Competencie Entrepreneurial Competence Significant at ∝ = 0.05 (t-table = 1.97) * Significant on ∝ = 0.05 (F-table = 3.07)
Hypothesis Testing. The use of Kappa in such a descriptive fashion for evaluating assessments does not preclude significance testing. For example, given that the expression for the standard error of Kappa under certain assumptions has been derived (Xxxxxx, Xxxxx, and Xxxxxxx, 1969), it is possible to test the null hypothesis that a value of Kappa equals zero (i.e., the value that would be obtained by chance). However, as illustrated by Henkel in the case of a chi-square test (Henkel, 1976), whether a finding is statistically significant or not (at a specific alpha level) is strongly dependent on the sample size. For example, a very low value of Kappa that is close to zero can be found statistically significant if the sample size is large enough. Conversely, a value of Kappa close to 1 may not be better than that obtained by chance if the sample size is very small. Therefore, it is also important to be able to interpret the value of Kappa irrespective of the sample size to decide if it is “clinically significant”. If the value of Kappa is greater than 0.62, and subsequent statistical testing fails to reject the null hypothesis, then there are different schools of thought that one can subscribe to depending on the consequences of an incorrect decision.11 For instance, in medicine, the drawing of conclusions from statistically insignificant results has been described as bordering on fraud (Xxxxxxx, 1990). On the other hand, in the social sciences some encourage the drawing of conclusions even from statistically insignificant results (Xxxxxx, 1955). In the context of the Kappa statistic, it has been suggested that hypothesis testing of Kappa is not needed because in practice agreement is usually better than chance anyway (Armitage and Xxxxx, 1994). Therefore, if one accepts the latter argument, then the most important issue is to interpret the actual value of Kappa rather than hypothesis testing. This argument is, however, a weak one in the context of software process assessments. Previous studies have shown that some obtained values of Kappa are not statistically significant due to extreme systematic bias by one of the assessors (Xx Xxxx, Xxxxxx, and Xxxxx, 1996). Perhaps a more parsimonious marriage of the above benchmark and hypothesis testing is to test the null hypothesis that κ ≤ 0.62. In the past a threshold of 0.4 was uncomfortably used (Xxxxxx, El Emam, and Xxxxx, 1997a). With the current benchmark, there is an initial rational basis for defining such a null hypothesis.
Hypothesis Testing. The heritability measuring the genetic sources affecting a phenotype is generally of more concern, and the genetic correlation describing the commonly shared genetic causes between the paired phenotypes is also the parameter of interest. With regard to Formula (4.8) for the computation of ERV, the ERV quantity has a non-zero value when both genetic correlation and heritabilities are non-zero and vice versa. For each pair of phenotypes, testing the null hypothesis H0 : ρ(12) = 0, h2 = 0, or h2 = 0 is the equivalent of assessing the statement of ERV(12) = 0, which gives the null hy- pothesis H0 : ERV(12) = 0 that needs to be examined during the significance testing. Regarding the selection of test statistic, we firstly consider using the LRT statistic by comparing the fit of the null model and that of the alternative model, as stated in Section 3.4.3, with the expression of T(12) = −2 × ΣA(E^RV(12)|Y) − A(E^RV(12)|Y)Σ, where E^RV(12) and E^RV(12) are estimated from the null and alternative models respectively. However, this commonly used LRT statistic is computationally very intensive for moderate to large sample sizes and nearly infeasible for the use of permutation inference. Therefore, we consider to employ the ERV estimator: T(12) = E^RV(12), as an alternative test statistic. The validity of this test statistic will be investigated with simulations in the later section.
Hypothesis Testing. According to the different objectives that were set in Section 3.1, different hypotheses are proposed. Starting from the organisational objectives, the following hypotheses should be tested. • The instrumentation does not alter PTW dynamics and rider driving behaviour. • The instrumentation is appropriate and all required parameters are recorded in a proper manner (no data fail, appropriate accuracy, synchronisation of the different data, etc.). • The data parameters and their accuracy are sufficient to provide a good quantitative and qualitative description of rider behaviour. • The procedure of data storage did not influence the course of the study, and was implemented efficiently. • The methodology for data analysis is appropriate and yields the requested output. • No legal or ethical issues are raised during the study and during data processing. Questions that should be answered are the following: • What are the lessons learned from the naturalistic riding study in respect to the above elements? • What is the most appropriate implementation time schedule for a large NRS? • How could the instrumentation be modified/integrated to improve the observation of riders’ behaviours? • How could other elements of the study (data storage, analysis methodology etc.) be modified/integrated to improve the observation of riders’ behaviours? • Was there a specific issue that was not taken into account which is a prerequisite for the successful implementation of a larger naturalistic riding study? Scientific hypotheses cannot be defined as one is not certain about the results of the study. However, several questions should be considered to achieve the aforementioned scientific objectives. • What rider patterns can be identified by the data? Could these patterns be correlated with specific rider profiles? – This question mainly corresponds to several of the “how” questions mentioned in section1. • How can one define and distinguish between riding under “normal” conditions or riding at conflicts? What are the parameters that one should record and are there specific values that could be set to define conflicts quantitatively? This would also allow setting triggers for conflicts. • What are the contributing factors and dynamic scenarios involved in conflicts? – This will also provide answers to the questions: “How do riders behave and cause an accident?” “How do riders behave to avoid an imminent accident?” and “How do riders behave in order to avoid getting into an accide...
Hypothesis Testing. The difference in the outcome of interest is calculated for each matched pair m (m= 1,2,...,M ) as ∆Ym = Ym,i − Ym,ij . The Wilcoxon signed rank test is used to test whether the difference across matched pairs is different from
