# 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.

methods

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.