Student’s of variance ANOVA. In this study, we mainly

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,

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.

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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.