The drug diflunisal is used to treat mild to moderate pain...

The drug diflunisal is used to treat mild to moderate pain due to osteoarthritis (OA) and rheumatoid arthritis (RA). The ocular effects of diflunisal had not been considered until a study was conducted on its effect on intraocular pressure in glaucoma patients who were already receiving maximum therapy for glaucoma [5]. The change in intraocular pressure after administration of diflunisal among 30 patients whose standard therapy was topical drugs only was $-0.7 \pm 2.1 \mathrm{mm}$ Hg. Assess the statistical significance of these results.

The drug diflunisal is used to treat mild to moderate pain due to osteoarthritis (OA) and rheumatoid arthritis (RA). The ocular effects of diflunisal had not been considered until a study was conducted on its effect on intraocular pressure in glaucoma patients who were already receiving maximum therapy for glaucoma [5].
The change in intraocular pressure after administration of diflunisal among 30 patients whose standard therapy was topical drugs only was $-0.7 \pm 2.1 \mathrm{mm}$ Hg. Assess the statistical significance of these results.

Key Concepts

t-Test

A t-test is a statistical method used to compare the sample mean to a known value (or between groups) when the data distribution is approximately normal and the sample size is relatively small. In the context of the question, a one-sample t-test would likely be employed to evaluate whether the mean change in intraocular pressure significantly differs from zero, accounting for both the observed mean change and its variability.

Standard Error of the Mean

The standard error of the mean quantifies the variability of the sample mean as an estimate of the true population mean. It is calculated by dividing the sample standard deviation by the square root of the sample size. This measure is used to assess how much the sample mean would vary from sample to sample, which is critical when determining the significance of the observed average change.

Hypothesis Testing

Hypothesis testing is a systematic method for evaluating a claim about a population parameter based on sample data. It involves formulating a null hypothesis that usually represents no effect or no difference (e.g., no change in intraocular pressure) and an alternative hypothesis that represents the expected effect. Statistical tests, such as the t-test, are then applied to determine whether the evidence is strong enough to reject the null hypothesis.

Statistical Significance

Statistical significance is a measure used in hypothesis testing to determine whether the observed effect in the data (such as a change or difference) is likely due to chance or reflects a real underlying phenomenon. It typically involves calculating a p-value, which if below a predetermined threshold (commonly 0.05), suggests that the observed effect is unlikely to have occurred by random variation alone.

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