Essay Example on Student's T Test compares the significant Difference

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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

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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 α α 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 γ 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 γ H2AX foci is 0 0955 which is larger than 0 05 that leads to decreased evidence against H0 However autophagy deficient cells revealed persistent γ 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 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



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