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

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. As a result, our average mean didn’t fall within the

significance region, which led us to accept the null hypothesis. However, the

probability and the significance level represent the likelihood of finding a

sample mean that would set in both tails of the distribution. Hereafter, the

significance levels and P values are key tools, that helped us to measure and decrease

this type of error in our hypothesis test.

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.