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 rejected if the p-value is less
than a predetermined level, ?.
? is called the significance level, and is the probability
of rejecting the null hypothesis given that it is true (a type I error). It is
usually set at or below 5%. and 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 you reject the null hypothesis.

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 <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. 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.