Laws of statistics indicate that surveys get more accurate with larger sample sizes

When you perform a survey, the intention is to get a representative image about a number of variables or statements within a certain target group or population. Due to practical reasons (too large, too expensive, too time-consuming,…) it is often difficult to interrogate the total population. In that case a sample is used. This is a selection of respondents chosen in such a way that they represent the total population as well as possible.

Inhaltsverzeichnis Show

The importance of having Large Sample Sizes for your research
Defining ‘large sample size’ / ‘large study’ by topic
Statistical importance of having a large sample size
Fields that benefit most from large sample sizes
Importance of a larger sample size from a publishing perspective
Determining an adequate sample size
Ensuring you have an adequately large study
Is secondary data generally more expensive or less expensive than primary data?
What is the total cost to conduct a market research survey?
Which of the following would be a source of primary data in a marketing research study?
Which is not a source of secondary data?

It is very important to use a correct sample size. When your sample is too big, this will lead to unnecessary waste of money and time. On the other hand, when it’s too small, your results will not be statistically significant and you will not come to reliable conclusions.

There are many different sampling methods. One of the most used is the random sample, where all members of the population have equal chances of being selected for the sample. On the support page of our site is a very useful and easy tool to calculate the minimal sample size needed for a survey conducted on a random sample. The calculation is based on the following parameters :

Size of the population
Here you have to enter the size of the group that has to be represented by the sample. If you conduct an employee survey for instance, your population would be the total staff. Once the population exceeds 20,000, your sample size will not change very much anymore.
Preferred margin of error
This is the positive or negative deviation you allow on your survey results for the sample, in other words the required precision level. Suppose in your survey 40% of the respondents pick a certain answer and your margin of error is 2%. This would mean that if you interrogate the total population, you can be sure that between 38% and 42% would pick the same answer. The smaller the allowed margin of error, the larger your sample will have to be.
Desired confidence level
The confidence level tells you how sure you can be of the margin of error, in other words how often the actual percentage of the population that picks a certain answer, lies within the margin of error. In market research, margins of error are calculated generally for a confidence level of 95%. This means the survey results will be in line with reality 19 out of 20 times. If you want a higher confidence level (e.g. 99%) your sample will have to be larger.

Once you have calculated the sample size, you know how many respondents you need to generate. Then you must still estimate how many individuals out of the population to ask to participate to insure the required number of respondents. For instance, if you send out email invitations and your sample size is 100, and the expected response rate is 20%, then you will have to send out 500 invitations.

After the data-collection phase of your survey you will know the actual number of respondents that have participated. Unless it happens to be the exact sample size you were looking for, you will then need to calculate the achieved margin of error.

Please note, the confidence level and margin of error calculated by our tool is for a random sample. Furthermore, it assumes the response pattern you receive is normally distributed. For sample sizes above 30, the normal distribution usually will be a good estimation of the actual way the responses are distributed (see also the central limit theorem). For smaller sample sizes the Student’s t-distribution is more appropriate, but is not supported by our sample size calculator.

The importance of having Large Sample Sizes for your research

Sample size can be defined as the number of pieces of information, data points or patients (in medical studies) tested or enrolled in an experiment or study. On any hypothesis, scientific research is built upon determining the mean values of a given dataset. The larger the sample size, the more accurate the average values will be. Larger sample sizes also help researchers identify outliers in data and provide smaller margins of error.

But just what is a ‘large scientific study’ with a ‘large sample size’?

Why are such studies important?

What type of research benefits most from large sample sizes?

And how can a researcher ensure they have an adequately large study?

Here, we discuss these various aspects of studies with large sample sizes.

Defining ‘large sample size’ / ‘large study’ by topic

The size of a ‘large’ study depends on the topic.

In medicine, large studies investigating common conditions such as heart disease or cancer may enrol tens of thousands of patients with multiple years of follow-up.
For specialty journals, ‘large studies’ may include clinical studies with hundreds of patients.
For highly specialised topics (such as certain rare genetic conditions), large patient populations may not exist. For such research, a ‘large’ study may enrol the entire known global population with the condition, which could be as few as dozens of patients.

Statistical importance of having a large sample size

Larger studies provide stronger and more reliable results because they have smaller margins of error and lower standards of deviation. (Standard deviation measures how spread out the data values are from the mean. The larger the study sample size, the smaller the margin of error.)
Larger sample sizes allow researchers to control the risk of reporting false-negative or false-positive findings. The greater number of samples, the greater the precision of results will be.

A useful primer that discusses the importance of sample size in planning and interpreting medical research can be found here.

Fields that benefit most from large sample sizes

Large sample sizes benefit many fields of research, including:

Medicine: Quality efficacy of treatment protocols, anatomic studies and biomechanical investigations all require large sample sizes. Ongoing COVID-19 vaccine trials depend on large volunteer patient populations.
Natural sciences: Long-term climate studies, agricultural science, zoology and the like all require large studies with thousands of data points.
Social sciences: Much social science research, public opinion and political polls, census and other demographic information, etc. rely heavily on large-scale survey studies.

Importance of a larger sample size from a publishing perspective

Academic publishers seek to publish research with the highest-quality, most-reliable and most-certain data. As an author, it is greatly to your advantage to submit manuscripts based on studies having as large a sample size as possible.

That said, there are limits to certainty and reliability of results. But that discussion would be beyond the scope of this article.

Determining an adequate sample size

In determining an adequate sample size for an experiment, you must establish the following:

Justifiable level of statistical significance
Chances of detecting a difference of given magnitude between the groups compared (the study’s power)
Targeted difference (effect size)
Variability of the data

Ensuring you have an adequately large study

Working with a biostatistician and experts familiar with study design will help you determine how large a study sample you need in order to determine a highly accurate answer to your specific hypothesis.

Note that not all research questions require massively large sample sizes. However, many do, and for such research, you may need to design, obtain funding for and conduct a multi-centre study or meta-analysis of existing studies.

Note: Multi-centre studies may come with many logistical, financial, ethical and analytical challenges. But when properly designed and executed, they provide some of the most definitive and highly cited publications.

Summary

One of the main goals of scientific research and publishing is to answer questions with as much certainty as possible. Ensuring large sample sizes in research studies would go a long way towards providing sufficient levels of certitude. Such large studies benefit numerous research applications in a wide variety of scientific and social science fields.

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Is secondary data generally more expensive or less expensive than primary data?

Primary data is very expensive while secondary data is economical. When working on a low budget, it is better for researchers to work with secondary data, then analyze it to uncover new trends. In fact, a researcher might work with both primary data and secondary data for one research.

What is the total cost to conduct a market research survey?

In general, you should plan to spend about $20,000 to $50,000 for a qualitative or quantitative custom market research project. Read more for the factors that contribute to overall market research cost.