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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
Central Limit Theorem01:14

Central Limit Theorem

The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...

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The large sample size fallacy.

Björn Lantz1

  • 1University of Borås, Borås, Sweden. bjorn.lantz@hb.se

Scandinavian Journal of Caring Sciences
|August 7, 2012
PubMed
Summary
This summary is machine-generated.

Statistical significance does not equate to practical significance. The large sample size fallacy, where large samples yield statistically significant but practically meaningless results, is prevalent in nursing research.

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Area of Science:

  • Nursing Research
  • Biostatistics

Background:

  • Statistical significance is often conflated with practical significance.
  • Large sample sizes can lead to statistically significant results with trivial effect sizes, a phenomenon known as the large sample size fallacy.
  • Effect size, not p-value, determines practical significance.

Purpose of the Study:

  • To examine the prevalence and implications of the large sample size fallacy in contemporary nursing research.
  • To highlight the importance of effect size in interpreting research findings.

Main Methods:

  • Literature review and analysis of nursing research articles.
  • Examination of how statistical significance and effect sizes are reported and interpreted.

Main Results:

  • Many nursing studies fail to report explicit effect sizes.
  • Statistical significance is frequently presented as the primary outcome, overshadowing practical implications.
  • A lack of qualitative discussion on the magnitude of observed effects is common.

Conclusions:

  • Researchers should routinely report and discuss effect sizes alongside p-values for statistically significant findings.
  • Qualitative interpretation of effect sizes, comparing them to existing literature, is crucial for establishing practical significance.
  • Addressing the large sample size fallacy is essential for advancing the rigor and applicability of nursing research.