Essay Example on Student's T Test compares the significant Difference

To test this hypothesis we harvested the samples from PCa cell lines as explained in 2 2 Cell culture and treatments and measured the results in order to decide whether the data from that experiment provides a strong evidence in order to reject the H0 or not If our evidence is strong to reject H0 then we are indirectly accepting the alternative hypothesis Ha which is autophagy inhibition is more effective for the PCa patients treatment combined with RT rather than RT treatment alone For each experiment we collected the samples data to define our hypothesis involving its finding by using the decision rule whether reject the null hypothesis or not The null hypothesis is rejected if the p value is less than a predetermined level α α is called the significance level and is the probability of rejecting the null hypothesis given that it is true a type I error It is usually set at or below 5 and the p value is a number between 0 and 1 and interpreted in the following way A small p value typically 0 05 indicates strong evidence against the null hypothesis so you reject the null hypothesis The probability of an outcome can be rejected when the p value is 0 05 In student s paired t test computed data of the difference between two samples before and after IR treatment were as followed calculating the mean by counting foci numbers nuclei that included 30 foci field 

The significance level 0 05 which indicates 5 of the difference exists in the distribution We can also see if it is statistically significant using the other common significance level of 0 01 This time our sample mean does not fall within the critical region and we fail to reject the null hypothesis This probability represents the likelihood of obtaining a sample mean that is at least as extreme as our sample mean in both tails of the distribution depending on the average mean Hence significance levels and P values are important tools that help us quantify and control this type of error in a hypothesis test Using these tools to decide when to reject the null hypothesis increases our chance of making the correct decision All assumptions should include appropriate positive and negative controls It is also valuable to distinguish between assessments that have a reproducible quantitative readout on how data will be tested across treatment groups for significance and rules for data exclusion Indeed it is difficult to predict a scenario where this would not benefit scientific rigor replicability and reduce bias One possible that needs to confirm biological replicates by using different samples are independent from another lab