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You did some research, but there are some problems. The results were not as you expected. However, you thought you were doing everything properly. So what exactly is the reason for this? Could you have made a sampling error?
If you ask what sampling error is, do not rush because it will be explained extensively in the rest of the article, but know this much: sampling error is something that every person is familiar with in the world of analysis, and you, unfortunately, have to deal with it.
A sampling error is a research problem that arises when a population studied does not actually reflect the entire population.
The main reason for this standard error is that the population sample is not compatible with the true population in terms of diversity and number. Although researchers include a margin of error in their research, sampling error is always an issue they must deal with.
Businesses often resort to analysis to get to a better position. However, when these analyses are not done carefully, they may cause some inaccuracies, such as sampling errors. The most frequently observed types of sampling errors are listed below:
Sampling error types in market research
An example of a sampling error will be given here. However, first of all, you should know how to calculate sampling error. You can generally use analysis programs and artificial intelligence as a sample error calculator, but it may still be useful to know the sampling error formula.
Sampling error = Z x STD/Sqrt (N)
Z- is the z-score corresponding to the desired confidence level (1.96 for a 95% confidence level).
STD- is the population standard deviation.
N- is the sample size.
For example, market research aims to reach the number of people using hats in summer. For this, a firm conducts a survey to estimate the proportion of people who wear hats during the summer season in a small town. They selected a random sample of 400 individuals and found that 120 of them reported wearing hats regularly during the summer. Researchers used the above formula to find the margin of error.
The margin of error for the estimated proportion of people who wear hats during the summer season is approximately 0.0448. This means that with a 95% confidence level, the true proportion of hat-wearers in the population is likely to fall within 4.48 percentage points of the observed proportion (30%) obtained from the sample.
Sampling errors are not the only statistical errors found in research; there are also non-sampling errors. Both of these negatively affect the outcome of the research. So, what exactly are the differences between these two?
In order for research to yield precise and reliable results, the margin of error must be quite low. This margin of error is generally considered acceptable between 5% and 3%. So, when you repeat a survey, the result must be more or less the same. Otherwise, there will be a sampling error, etc. There may be an error. So, how should you take precautions against this error?
How to reduce the sampling error
In this section, you can easily find what you are curious about and want to learn more about sampling error.
En biologie, on parle d'erreur d'échantillonnage lorsque des échantillons d'organismes vivants, de tissus ou de cellules ne correspondent pas aux caractéristiques de la population générale. Cette incohérence est due à une sélection incorrecte ou incomplète des échantillons. La réduction de l'erreur d'échantillonnage est indispensable pour que les analyses statistiques biologiques soient plus fructueuses.
Une erreur, comme son nom l'indique, est une situation indésirable. Elle nuit à la santé de toute recherche. Tout d'abord, elle rend les prévisions et les calculs inexacts et incomplets. Par conséquent, elle réduit la précision des résultats. Elle entraîne une perte de temps et d'argent car la recherche devra être rééchantillonnée et éditée à nouveau.
Éviter les erreurs d'échantillonnage est en fait assez simple. Il n'est pas nécessaire d'avoir de vastes connaissances pour ce travail ; il suffit de faire ce qui suit.
Veillez à ce que la taille de l'échantillon soit toujours importante
Éviter les groupes homogènes et appliquer un échantillonnage aléatoire contrôlé
Déterminez bien l'objet de votre recherche et la base d'échantillonnage.
Réalisez des études pilotes
Demandez l'aide de statisticiens experts
Veillez à ce que la recherche soit toujours valide.
L'erreur d'échantillonnage est, dans l'ensemble, le reflet d'une recherche superficielle et d'un chercheur inexpérimenté. Pour expliquer davantage ces raisons, on peut citer, par exemple, le fait de laisser le hasard décider du déroulement de la recherche, de ne pas regrouper les groupes cibles ou de maintenir une taille d'échantillon réduite, de poursuivre la recherche sans méthodologie ni enregistrement, d'utiliser les techniques d'analyse de manière inadéquate et de collecter des données incorrectes.
As a result, sampling errors are misleading data collection and analysis errors for the target population. You need to avoid this so that your research can provide accurate results.
This article explains the definition and types of sampling errors with examples. It also shows the formula calculation you can use for sampling error. Its difference from non-sampling error and how you can minimize sampling error are explained. Thus, you now have more information about sampling error.
Sena is a content writer at forms.app. She likes to read and write articles on different topics. Sena also likes to learn about different cultures and travel. She likes to study and learn different languages. Her specialty is linguistics, surveys, survey questions, and sampling methods.