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 < 0.01*, P < 0.001**, P < 0.0001***. The multiple t-test compares the
statistical significance probabilities analysis for several t-tests at once. The
two-way ANOVA used to compare independent variables of interest and to
understand if there is an interaction between them in different conditions.
Our hypothesis findings needed more common hypothesis
tests such as two-way analysis of variance ANOVA. In this study, we mainly have
two independent factors that are autophagy and IR with different time points. This
basic research begins with a question that whether autophagy inhibition is more
effective for the PCa patients' treatment combined with radiotherapy (RT) rather
than RT treatment alone. To test this question, we need to transform basic
question to a testable hypothesis, labeled H0 named as a Null
hypothesis, which takes the following form: H0: Whether autophagy
inhibition is NOT more effective for the PCa patients' treatment combined with RT
rather than RT treatment alone. 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 accepted if the p-value is larger
than the significance level, ?.
? is called the significance level, and P value is the
probability of rejecting or accepting the null hypothesis (a type I error),
that It is usually set at equal or less than the 5%. 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 we
reject the null hypothesis. 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 <0.0001, this is much less
than 0.05, hence the evidence against H0 is strong and it can be
rejected.
Under the assumption that the null hypothesis
is true, we repeated large number of random samples (>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.