Student’s statistical significance probabilities analysis for several

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.

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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. 

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