Student’s T Test compares the significant difference

between two groups (two means). In this study, paired t-test is used to compare

groups and test the significant difference between two sets of data. If the

data are significant given by the P ??0.05 were considered as significant data,

P

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. Each experiment

was repeated 3 times as indicated by (n=3), to allow calculation of the average

mean of the gathered data. For example, H0: autophagy has no role on

the DNA-damage response (DDR) signaling in response to ionizing radiation (IR)

treatment. In contrast, Ha: autophagy regulates the DDR signaling in response

to IR treatment; we examined it in autophagy-deficient PCa cells.

Immunostaining showed that the number of ?H2AX IR-induced foci (IRIFs) at 0.5h

were not significantly different between dox-pretreated cells followed by IR

compared to IR treatment alone in LNCaP (Fig 3.2. a and b). To explain it

statistically, the probability of forming ?H2AX foci is 0.0955, which is larger

than 0.05, that leads to decreased evidence against H0. However,

autophagy-deficient cells revealed persistent ?H2AX foci at 24h following IR

treatment compared to the parental cells following IR alone. The probability of

which is 30 foci/field) to test

H0 and Ha. The significance level (a)=

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.