Conclusions from significance tests

Carmichael, N.G., H. Enzmann, I. Pate, and F. Waechter, 1997. The significance of mouse hver tumor formation for carcinogenic risk assessment results and conclusions from a survey of ten years of testing by the agrochemical industry. Environ. Health Perspect. 105 1196-1203. 

In the next step a value for the test is calculated from the data and compared with the tabulated critical value. If the calculated value exceeds the critical value this indicates significance. To finalize the significance test the test statistics have to be evaluated with respect to the Null hypothesis. This enables us to make decisions and to draw conclusions. 

A significant p-value from this test would cause us to reject the null hypothesis, but the conclusion from this only tells us that there are some differences somewhere at least two of the ps are different. At that point we would want to look to identify where those differences lie and this would lead us to pairwise comparisons of the... 

It is difficult to arrive at a definite conclusion from this work. The major criticism which has been made of this and similar testing is that at least 15 samples are required in order to provide statistical significance at 0.5 % sucrose based on cane yield. Variations in the yield from field blocks are commonly as high as 10%, and often more. 

As this calculated test statistic is in the critical region (t = -3.10 < -2.26) the null hypothesis is rejected. The result is considered statistically significant at the a = 0.05 level because there was less than a 5% chance of such a result being observed by chance alone. The conclusion from the study is that the new drug did lower SBP by a mean of 7 mmHg. Scientists from the sponsor company may use this information as sufficient preliminary evidence to continue with the development of the new drug. 

The following conclusions from these tests seem to be of general significance ... 

In Section 2.1, we used statistics only for the description of data. In many cases, however, it is necessary to draw conclusions from comparisons of data at a given statistical significance. These test methods are part of inferential statistics. For testing hypotheses, we need to learn about some more distributions, such as the t-, F-, and -distributions. 

Conclusions from these tests were that 219 K and ambient exposure has no significant effect upon PPQ and... 

This section looks more closely at the conclusions that may be drawn from a significance test. As was explained in Section 3.2, a significance test at, for example, the P = 0.05 ievei invoives a 5% risk that a null hypothesis will be rejected even though it is true. This type of error is known as a Type I error. The risk of such an error can be reduced by altering the significance level of the test to P= 0.01 or even P= 0.001. This, however, is not the only possible type of error it is also possible to retain a null hypothesis even when it is false. This is called a Type II error. In order to calculate the probability of this type of error it is necessary to postulate an alternative to the null hypothesis, known as an alternative hypothesis, Hj. 

Sea water for the tests was provided by an all-plastic, recirculating system. This consisted of a reservoir of approximately 113 liters capacity with a flow rate to each specimen bath of approximately 7.6 liters per minute. This reservoir was replenished on a twice-weekly basis. The sea water was transported in plastic containers to the on-campus test laboratory from the FAU Marine Materials and Corrosion Laboratory, where a filtered, once-through natural sea water supply is available. Various properties of this water have been monitored daily, and data for an annual period have been reported [6]. This revealed no significant departure of these parameters from values typical of semltropical surface water. Periodic monitoring of the water in the recirculating fatigue system yielded this same conclusion. 

Early on, Goddard showed in several himdred tests that pitting of aluminum in freshwater followed a cube-root curve d, = where d, is the maximum pit depth at time f/. The time to penetration can then be calculated by the formula di). The significant conclusion from the cube-root... 

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