The main aim of randomisation in a clinical trial is to remove the bias and avoid any potential confounding variables. While in double blind studies both the investigators and the patients are not aware of which group they belong in, being blind is not essential in carrying out a randomized study, nor is it essential that the randomisation be done away from the study centre. A placebo also does not facilitate randomisation, which can be done in single centre and multi-centre trials.

An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional and cohort study designs as well (with some modifications and/or assumptions). Wherea = Number of exposed casesb = Number of exposed non-casesc = Number of unexposed casesd = Number of unexposed non-casesOR=(a/c) / (b/d) = ad/bc

The Chi-squared test evaluates if two variables are related. The other statistical tests mentioned do not perform this function.

The P value is defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance. Being a probability, P can take any value between 0 and 1. Values close to 0 indicate that the observed difference is unlikely to be due to chance, whereas a P value close to 1 suggests no difference between the groups other than due to chance. Thus, it is common in medical journals to see adjectives such as “highly significant” or “very significant” after quoting the P value depending on how close to zero the value is.A lower p-value is sometimes interpreted as meaning there is a stronger relationship between two variables. However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).To understand the strength of the difference between two groups (control vs. experimental) a researcher needs to calculate the effect size.

The equation, lung function = 3.2 + (1.7 x height) is linear, but this does not necessarily mean that the relationship itself is linear. The equation is able to predict that taller individuals are more likely to have greater lung functions. No information is given on whether the coefficients and the R value are statistically significant.

The Chi squared test is used to find the relationship between two variables, determining whether they are not independent of each other. This test of statistical significance allows you to determine the degree of confidence with which you can accept or reject the null hypothesis. The student t test compares the significant differences in data means, while rank correlation, linear regression and the correlation coefficient all help to measure the linear relationship between variables.

If the 95% confidence interval contains zero (more precisely, the parameter value specified in the null hypothesis), then the effect will not be significant at the 0.05 level.If the 95% CI for the DIFFERENCE between the 2 groups contains the value 0, this means that the p-value will be greater than 0.05. Conversely, if the 95% CI does not contain the value 0, then the p-value will be strictly less than 0.05. The same applies when comparing groups using a ratio, such as an odds ratio or risk ratio. When using a RATIO instead of a DIFFERENCE, the situation of no difference between the 2 groups will be indicated by a value of 1 instead of 0. If the ratio equals to 1, the 2 groups are equal. Hence, if the 95% CI of the ratio contains the value 1, the p-value will be greater than 0.05. Alternatively, if the 95% CI does not contain the value 1, the p-value is strictly less than 0.05.

Box plots can be used to display numerical outcomes and give valid summaries (median and interquartile range or IQR) for any distributional form that the outcomes might take. Additionally, they show outlying values and provide data summaries that are not unduly influenced by those outliersOther options:- The box itself represents the interquartile range, with the two whiskers representing the variability outside the upper and lower quartiles.- Variability is represented with whiskers and feathers are used in fan charts.They are not as informative as showing the actual values but can be used to make comparisons of medians and IQRs between subgroups.

The paired sample t-test, sometimes called the dependent sample t-test, is a statistical procedure used to determine whether the mean difference between two sets of observations is zero. In a paired sample t-test, each subject or entity is measured twice, resulting in pairs of observations. Common applications of the paired sample t-test include case-control studies or repeated-measures designs.Statistical significance is determined by looking at the p-value. The p-value gives the probability of observing the test results under the null hypothesis. The lower the p-value, the lower the probability of obtaining a result like the one that was observed if the null hypothesis was true. Thus, a low p-value indicates decreased support for the null hypothesis. However, the possibility that the null hypothesis is true and that we simply obtained a very rare result can never be ruled out completely. The cut-off value for determining statistical significance is ultimately decided on by the researcher, but usually a value of .05 or less is chosen. This corresponds to a 5% (or less) chance of obtaining a result like the one that was observed if the null hypothesis was true.

Case control studies are observational studies which retrospectively determine whether a patient might have been exposed to a risk factor for a certain disease, as compared to a control group from the general population. They can be used for a range of outcomes as well as rare diseases. One of the biggest problems with this type of study is recall bias. Research subjects may selectively remember factors that are more predictive of the disease outcome when compared to the control group. All studies should have prior power calculations before the study commences.