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What are the best ways to ensure a representative sample size?

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Sampling is a crucial step in any statistical analysis, as it allows you to draw conclusions about a larger population based on a smaller subset of data. However, not all samples are created equal. Some samples may be biased, unrepresentative, or too small to produce reliable results. How can you avoid these pitfalls and ensure a representative sample size for your research? Here are some best practices to follow.

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Define your population:

Clarify who you're studying. Whether it's all adults with hypertension in a region or a specific age group, getting this right shapes your entire sampling strategy, ensuring relevance and accuracy.
Choose your sampling method:

Selecting the right method is crucial. Probability sampling gives each member a fair chance to be included, which boosts your study's legitimacy. This way, you can confidently draw conclusions about your whole population.

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Dr Patrick Druggan

Helping others build a better future -…
Maarten Boers

Consultant for Clinical Research and…

1 Define your population

Before you can select a sample, you need to define your population of interest. This is the group of individuals or units that you want to generalize your findings to. For example, if you are studying the effects of a new drug on blood pressure, your population may be all adults with hypertension in a certain region. You should specify the criteria that define your population, such as age, location, diagnosis, or any other relevant factors. This will help you to determine the scope and feasibility of your sampling strategy.

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Maarten Boers

Consultant for Clinical Research and Data Visualization
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I think the question should be split into: - representative - size Also, the term 'representative' is linked to studies that try to determine population characteristics. In studies on the usefulness of diagnostic tests, or on the effect of treatment, this term is less used, and other requirements apply.

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2 Choose your sampling method

Once you have defined your population, you need to choose a sampling method that suits your research question and design. There are two main types of sampling methods: probability and non-probability. Probability sampling methods involve selecting a sample at random from the population, ensuring that each unit has a known and equal chance of being included. This reduces the risk of bias and allows you to calculate the sampling error and confidence intervals for your estimates. Some common probability sampling methods are simple random sampling, stratified sampling, cluster sampling, and systematic sampling.

Non-probability sampling methods involve selecting a sample based on convenience, availability, or judgment, without using randomization. This means that the sample may not be representative of the population, and you cannot measure the sampling error or the confidence level of your estimates. However, non-probability sampling methods may be useful in some situations, such as exploratory research, qualitative research, or when probability sampling is not feasible or ethical. Some common non-probability sampling methods are convenience sampling, quota sampling, purposive sampling, and snowball sampling.

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Dr Patrick Druggan

Helping others build a better future - faster
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You have confused two things. A representative populations, and a sample that meets the requirements for powering your study. They might seem similar but they are not. A representative population would cover the ethnic groups , genders and age groups in a society. The size would be determined by the data type and methods you were using, the critical p-value and power. That is also dependent on the number of replicates you intend to carry out to verify the findings

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3 Determine your sample size

The next step is to determine how many units you need to include in your sample to achieve your desired level of precision and accuracy. The sample size depends on several factors, such as the population size, the sampling method, the margin of error, the confidence level, and the variability of the data. There are various formulas and calculators that can help you to estimate the sample size for different scenarios. However, you should also consider the practical and ethical limitations of your sampling process, such as time, cost, availability, and consent.

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Eren Unlu

Co-Founder
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Any statistical work which does not base its sample size, effect size, confidence intervals etc on proper hypothesis formulation would fail to get credibility in sensitive fields. It is essential to report details about assumptions and indicators.

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4 Collect and evaluate your data

After you have selected your sample, you need to collect and evaluate your data according to your research plan. You should check the quality and validity of your data, and identify any missing, inaccurate, or inconsistent values. You should also compare your sample characteristics with the population characteristics, and assess the representativeness and generalizability of your sample. If you find any significant deviations or discrepancies, you may need to adjust your sampling method, increase your sample size, or revise your conclusions.

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Maarten Boers

Consultant for Clinical Research and Data Visualization
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these sections are not about the sample, but an incomplete summary of how to do scientific research. Probably best deleted here.

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5 Report your results

Finally, you need to report your results in a clear and transparent way, and acknowledge any limitations or assumptions of your sampling method. You should describe how you defined your population, how you selected your sample, how you calculated your sample size, and how you collected and evaluated your data. You should also provide the relevant statistics, such as the sampling error, the confidence interval, and the response rate. This will help your readers to understand and interpret your findings, and to evaluate the credibility and reliability of your research.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Eren Unlu

Co-Founder
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In real world use cases, especially imvolving humans, you will eventually understand that perfect subsampling of a population is a mere dream. You will encounter many steps in the flow that you would need to readjust methodology to in order to sustain representability.

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Abdulrahman Alshanqiti, ACSI, CME4

Equity Analyst | Portfolio Manager | Advisor | Biostatistician | Science/Financial Writer.
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In scientific research, your results are more than just the p-value. They should provide context, real-world relevance, and transparency in methodology. Your findings should be a comprehensive narrative, contributing to the field's knowledge and impacting decisions beyond statistical significance. Though in my experience, sometimes, your whole research gets thrown out of the window because of a few basis points.

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What are the best ways to ensure a representative sample size?

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3

4

5

6

1 Define your population

2 Choose your sampling method

3 Determine your sample size

4 Collect and evaluate your data

5 Report your results

6 Here’s what else to consider

Statistics

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What are the best ways to ensure a representative sample size?

1

2

3

4

5

6

1 Define your population

2 Choose your sampling method

3 Determine your sample size

4 Collect and evaluate your data

5 Report your results

6 Here’s what else to consider

Statistics

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