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<\/p>\n<\/p>\n\nWelcome to our biostatistical analysis series. Today, we have an interesting topic lined up – the top 10 commonly confused words in this field. Let’s dive right in!\n<\/p>\n
\nBias refers to a consistent deviation from the true value, while variability represents the spread of data points. Understanding the distinction is vital, as biased data can lead to incorrect conclusions, while high variability may indicate instability in the results.\n<\/p>\n
\nSensitivity measures a test’s ability to correctly identify positive cases, while specificity gauges its accuracy in identifying negative cases. Both are crucial in diagnostic tests, and a balance between the two is often desired.<\/p>\n
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\nType I error, also known as a false positive, occurs when we reject a true null hypothesis. Type II error, or a false negative, happens when we fail to reject a false null hypothesis. Balancing these errors is essential in hypothesis testing.<\/p>\n
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\nPower is the probability of correctly rejecting a false null hypothesis. It depends on factors like effect size, significance level, and sample size. Increasing the sample size generally enhances the study’s power.\n<\/p>\n
\nParametric tests assume a specific distribution, often the normal distribution, while non-parametric tests make fewer assumptions about the data’s distribution. The choice between the two depends on the data’s characteristics and the research question.\n<\/p>\n
\nConfounding occurs when a variable is associated with both the exposure and outcome, leading to a false association. Effect modification, on the other hand, suggests that the effect of an exposure on the outcome varies based on another variable.\n<\/p>\n
\nOdds ratio measures the odds of an event occurring in one group compared to another, while relative risk quantifies the risk of an event in one group relative to another. Odds ratio is commonly used in case-control studies, while relative risk is prevalent in cohort studies.\n<\/p>\n
\nThe p-value indicates the probability of obtaining the observed results by chance alone, assuming the null hypothesis is true. A confidence interval, on the other hand, provides a range of plausible values for the population parameter. Both are important in drawing conclusions from data.\n<\/p>\n
\nStratified sampling involves dividing the population into homogeneous groups and randomly selecting samples from each group. Cluster sampling, on the other hand, involves randomly selecting groups or clusters and including all individuals within the selected clusters.\n<\/p>\n
\nA cross-sectional study examines a population at a specific point in time, providing a snapshot. A longitudinal study, on the other hand, follows a population over time, allowing for the analysis of trends and changes.<\/p>\n","protected":false},"excerpt":{"rendered":"