What are inferential statistics? (with photo)

Researcher asking questions to a participant.

Inferential statistics are data used to make generalizations about a population based on a sample. They rely on the use of a random sampling technique designed to ensure that a sample is representative. A simple example of an inferential statistic can likely be found on the front page of almost any newspaper, with any article claiming that “X% of population Y thinks/does/feels/believes Z.” A statement like “33% of 24-30 year olds prefer cake to pie” is based on inferential statistics. It would be impractical to ask every person aged 24 to 30 about their dessert preferences, so instead, a representative sample of the population was surveyed with the aim of making an inference about the population as a whole.

Inferential and Descriptive Statistics

Another way to use research data is descriptive statistics. In this case, statements are made that simply describe the data collected. It is possible that the same dataset will be used descriptively or inferentially. For example, in the run-up to a US election, 1,000 people in a city may be asked about their voting intentions, with the result that 430 say they would vote Democrat, 410 said they would vote Republican, with 160 undecided or unwilling to vote. tell . An example of using this data descriptively would be to simply state that 43% of 1,000 people surveyed in this city intend to vote Democrat. An inferential statement would be “Democrats have a 2% lead” – an inference about overall voting intentions was drawn from a sample.


Before drawing any general conclusions from a sample, it is important to employ the correct methods, otherwise these conclusions may not be valid. Common sources of error are in the way the sample is composed, and several factors can influence the validity of the sample population. Size is critical, as the smaller the size, the greater the risk that the sample is not representative of the population as a whole. Care must be taken to eliminate sources of prejudice. In the example above, factors such as age, sex and income can have a considerable influence on voting intentions, so if the sample was not composed in a way that reflects the general population, the conclusion may not be valid.

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Sampling methods must be chosen carefully; for example, if someone took a convenience sample that included every ten names in the phone book or every ten passers-by in a mall, that sample might not be valid. Sample bias is also a consideration. For example, it is possible that 24- to 30-year-olds who attend a pie lover’s convention are more likely to like pie than cake, which would mean that a survey of dessert preferences that used conference attendees as a sample would not be very representative.


The use of inferential statistics is the basis of research on populations and events because it is often difficult, and often impossible, to survey each member of a population or to observe each event. Instead, researchers try to get a representative sample and use that as a basis for more general conclusions. For example, it would not have been possible to check every smoker’s medical records to establish a link between smoking and lung cancer, but numerous random samples comparing smokers with nonsmokers and eliminating other risk factors have firmly established that link.

Researchers working with inferential statistics seek to keep their methods and practices transparent and as rigorous as possible to ensure the integrity of their results. Statements based on informal searches and quick searches may not be very helpful, but in areas such as medical research and clinical trial standards they are much stricter, and inferential statistics have provided vast amounts of valuable information. In other areas, they are used every day to make sweeping generalizations about populations that can shape public policy, product design, marketing, and political campaigns.

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