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1. SAMPLING CONCEPTS PART 5 BY SALLY PHIRI( STATISTICIAN) Concept of Statistics 1 Introduction to Statistics for Clinicians

2. Learning Outcomes Concept of Statistics 2 Understand the need and advantages of sampling. Describe the sampling distribution of means. Apply the Central Limit Theorem. Explain the sampling distribution of percentages. Use sampling techniques in clinical research. Calculate key sampling metrics such as standard error and proportion

3. Understand the need and advantages of sampling DATA 3 Sampling involves selecting a subset of individuals from a population to make conclusions about the entire population Reduces time and cost compared to studying the whole population. Allows for faster data collection, which is important in clinical trials. Helps when studying populations that are too large or geographically dispersed. A clinician studying the prevalence of hypertension cannot survey every patient in the country, so a representative sample of 1,000 patients is taken

4. Advantages of Sampling Summarizing Data part 1 4 Cost-Effective: Collecting data from a sample is cheaper than surveying an entire population. Example: Conducting a study on 100 patients vs. 1,000 patients reduces research costs. Faster Data Collection: A smaller sample size speeds up data collection and analysis. Example: A smaller clinical trial can yield faster preliminary results.

5. Sampling Distribution of Means DATA 5 . The distribution of sample means obtained from repeated sampling from a population. The mean of the sample means equals the population mean, provided the samples are unbiased. A population has an average blood pressure of 120 mmHg. Multiple samples (n = 30) taken from this population will have a sampling distribution with a mean of around 120 mmHg.

6. Central Limit Theorem (CLT) Concept of Statistics 6 The CLT states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's original distribution As sample size 𝑛 increases, the sample means become normally distributed. The CLT allows us to make inferences about population parameters using the normal distribution. If we repeatedly take samples of 50 patients’ cholesterol levels, the distribution of the sample means will approximate a normal distribution, even if the original cholesterol levels are skewed.

7. Sampling Distribution of Percentages 2016/17 Summarizing Data part 1 7 The sampling distribution of percentages is the distribution of sample proportions obtained from multiple samples of the same size from a population. In a population where 60% of patients have hypertension, if you take multiple random samples (n = 100), the distribution of sample percentages (proportion of hypertensive patients) will cluster around 60%.