What are inferential stats?

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Inferential statistics are data that is 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 inferential statistics can probably be found on the front page of almost any newspaper, with any article stating that “X% of population Y thinks/does/feels/believes Z”. A statement like “33% of 24-30 year olds prefer pie to pie” is based on inferential statistics. It wouldn’t be practical to ask every single 24-30 year old about their dessert preferences, so instead, a representative sample of the population was surveyed with the goal of making an inference about the population as a whole.

Inferential and descriptive statistics
Another way to use survey data takes the form of descriptive statistics. In this case, statements are made that simply describe the data collected. You can use the same data set descriptively or inferentially. For example, in the run-up to a US election, 1,000 people in a city could be polled about their voting intentions, resulting in 430 saying they would vote Democrat, 410 saying they would vote Republican, with 160 undecided or not willing to say . An example of using this data descriptively would be to simply state that 43% of 1,000 people polled in this city plan to vote Democrat. An inferential statement would be “Democrats have a 2% lead” – an inference about voting intentions in general was drawn from a sample.

Methods
Before drawing general conclusions from a sample it is important to use the correct methods, otherwise these conclusions may not be valid. Common sources of error are in how the sample is composed, and a number of factors can affect the validity of the sample population. Size is critical, because the smaller the size, the greater the risk that the sample is not representative of the population as a whole. Care should also be taken to eliminate sources of bias. In the example above, factors such as age, gender, and income can have a considerable influence on voting intentions, so if the sample was not composed to reflect the general population, the conclusion may not hold.

Sampling methods must be chosen carefully; for example, if someone took a convenience sample that included every tenth name in the telephone book or every tenth passer-by in a mall, this sample might not be valid. Sample bias is also a consideration. For example, it is possible that 10- to 10-year-olds attending a cake-lovers convention are more likely to enjoy pie than cake, which would mean that a survey of dessert preferences using conference attendees as sample would not be very representative.

it is used
The use of inferential statistics is a cornerstone of population and event research, because it is usually difficult, and often impossible, to census every member of a population or observe every event. Instead, researchers try to obtain a representative sample and use that as a basis for more general conclusions. For example, it would not have been possible to check the medical records of every single smoker to establish a link between smoking and lung cancer, but numerous random samples comparing smokers and non-smokers and eliminating other risk factors have firmly established this link.

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

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