PSY 301-001 Research Methods & Statistics Module 9: Hypothesis Testing/ Inferential Statistics

Null hypothesis significance testing (NHST)

A procedure for statistical inference used to decide whether a variable has produced an effect in a study. NHST begins with the assumption that the variable has no effect (see null hypothesis), and probability theory is used to determine the probability that the effect (e.g., a mean difference between conditions) observed in a study would occur simply by error variation ("chance"). If the likelihood of the observed effect is small (see level of significance), assuming the null hypothesis is true, we infer the variable produced a reliable effect (see statistically significant).

Inferential statistics

A set of techniques that uses the laws of chance and probability to help researchers make decisions about the meaning of their data and the inferences they can make from that information.

Null hypothesis

A statement or idea that can be falsified, or proved wrong. Null means "nothing," and colloquially, a null hypothesis means "assume that nothing is going on."

According to the central limit theorem, the mean of the sampling distribution of the mean will be ...

Equally to the population mean.

When the sample size is large enough, the sampling distribution of any statistic will be ...

Normally distributed

Confidence interval

Provides a range which is likely to include the true population value (e.g., the mean of a population or a difference between 2 means in the population).

Inferential statistics is the process of using data from a _____________ whose characteristics are known, to make inferences about some _______________ whose characteristics are often unknown.

Sample; population

The standard deviation of a sampling distribution of the mean is called the ...

Standard error of the mean

Inferential statistics are based on ...

The laws of chance and probability.

Power

The likelihood of not making a Type II error when the null hypothesis is false.

Alpha level

The point at which researchers will decide whether the p is too high (and therefore will retain the null hypothesis) or very low (and therefore will reject the null hypothesis).

Type II error (beta error)

The probability of failing to reject a null hypothesis that is, in fact, false.

Alpha is ...

The probability of making Type I error.

Type I error (alpha error)

The probability of rejecting a null hypothesis that is, in fact, true.

Power can be defined as ...

The probability of rejecting the null hypothesis when the null hypothesis really is false.

True or False: A Type I error is made when one decides that there is a real effect present in the data (i.e. the null hypothesis is rejected), when in reality the difference is due to chance variation (i.e. the null hypothesis is actually true).

True

True or False: If you are analyzing data from an experiment you have conducted, and you fail to reject the null hypothesis, then you should conclude that there was not enough evidence to say that there was a real effect of the independent variable on the dependent variable.

True

True or False: If you are analyzing data from an experiment you have conducted, and you reject the null hypothesis, then there was probably a real effect of the independent variable on the dependent variable.

True

Cohen's d

When a study involves group or condition means (such as the mean amount of popcorn eaten from a large container compared with the mean amount from a medium container), we can describe the effect size in terms of how far apart the two means are, in standard deviation units.