# Inferential Statistics

## Presentation on theme: "Inferential Statistics"— Presentation transcript:

Inferential Statistics
Chapter 13

Inferential Statistics
Inferential stats are used to determine whether we can make statements that the results found in the present experiment reflect a true difference in the entire population of interest and not just the sample used in the experiment. Therefore inferential statistics allow us to make predictions about the entire population based on the findings of sample groups. Inferential statistics give a probability that the difference between the two means from the sample used in the experiment represents a true difference based on the manipulation of the IV, and not random error.

Null and Research Hypotheses
Null hypothesis  states simply that the population means (after conducting the experiment) are equal and that any observed differences are due to random error. Alternative hypothesis  states that the population means are not equal and therefore the treatment or independent variable had an effect. Statistical significance  indicates that there is a low probability that the difference between the obtained sample was due to random error. Alpha level -pre-determined probability level used to make a decision about statistical significance.

Probability and Sampling distributions
Probability  likelihood of the occurrence or some event or outcome. Statistical Significance—is a matter of probability. Sampling distribution  probability distributions based on many different samples taken over and over and shows the frequency of different sample outcomes from many separate random samples. Sampling distribution- is based on the assumption that the null hypothesis is true. Critical Values are obtained from Sampling Distributions and they are calculations of probability based on sample size and degrees of freedom

Sample Size The sample size also has an effect on determining statistical significance. The more samples you collect, the more likely you are to obtain an accurate estimate of the true population value Thus, as your sample size increases, you can be more confident that your outcome is actually different from the expectations of the null hyp

Differential Statistics
T-tests and F-tests are differential statistics because they detect differences between groups. The sampling distribution of all possible t values has a mean of 0 and a standard deviation of 1 It reflects all the possible outcomes we could expect if we compared the means of two groups and the null hypothesis is correct

T-test The calculated t value is a ratio of two aspects of the data
The difference between the group means The variability within groups Group difference  difference between your obtained means Under the null hypothesis you expect this difference to be 0. The value of t increases as the difference betweent your obtained sample means increases Within-group variability  the amt of variability of scores about the mean

T-test Formula t = group difference within-group variability
The numerator of the formula is the difference between the means of the two groups The denominator is the variance (s2) of each group divided by the number of Ss in the group, which are added together The square root of the variance divided by the number of subjects = standard deviation Finally, we calculate our obtained t value by dividing the mean difference by the SD You would then compare your obtained t to those listed in the t-table of critical values to determine if it is significant or not

One Tailed d vs. Two-Tailed Tests
A one-tailed test is conducted if you are interested only in whether the obtained value of the statistic falls in one tail of the sampling distribution for that statistic. --This is usually the case when your research hypothesis is directional. ---Group one will score higher than group two. ---The critical region in a one-tailed test contains 5% of the total area under the curve (alpha = .05)

Two Tailed Test Two-tailed test if you wanted to know whether the new therapy was either better or worse than the standard method. You need to check whether your obtained statistic falls into either tail of the distribution There are two critical region in a two-tailed test To keep the probability at .05, the total percentage of cases found in the two tails of the distribution must equal 5% Thus each critical region must contain 2.5% of the cases So the scores required to reach statistical significance must be more extreme than was necessary for the one-tailed test

When to use a one vs. two tailed?
Major implication - for a given alpha level, you must obtain a greater difference between the means of your two treatment groups to reach statistical significance if you use a two-tailed test than if you used a one-tailed test The one-tailed test is more likely to detect a real difference if one is present (that is, it is more powerful) However, using a one-tailed test means giving up any info about the reliability of a difference in the other, untested direction The general rule of thumb is: Always use a two-tailed test unless there are compelling

F-test The analysis of variance or F test is an extension of the t test When a study has only one IV, F and t are virtually identical—the F = t-squared ANOVA is used when there are more than two levels of an independent variable The F statistic is a ratio of two types of variance: Systematic variance  the deviation of the group means from the grand mean or the mean score of all individual groups Error variance  the deviation of the individual scores in each goup from their respective group means The larger the F value, the more likely the score is significant

Effect Size Effect size  quantifies the size of the difference between groups If we have two grps, the effect size is the difference between the groups expressed in standard deviation units. Therefore, the effect size is between O and 1. The effect size indicates the strength of the relationship. The closer to one, the stronger the relationship. The advantage of the effect size is that it is not a function of the sample size

Type one and Type two errors
Type I error  occurs when the researcher says that a relationship exists when in fact it does not You have falsely rejected the null hyp Type II error  occurs when the researcher says that a relationship does not exist, when in fact it does You have falsely accepted the null hyp

True State of Affairs Null is true Null is False C Reject Null
Type I error alpha Correct Decision 1-beta Accept Null Correct Decision 1-alpha Type II error beta

Probability of Type II error
If we set a low alpha level to decrease the chances of a Type I error (accepting a hypothesis that is true when it is not (e.g., p<.01), we increase the chances of a Type II error True differences are more likely to be detected if the sample size is large. If the effect size is large, a Type II error is unlikely

Interpreting non-significant results
Negative or nonsignificant results are difficult to interpret There are several causes for nonsignificant results: The instruction could be hard to understand Have a weak manipulation of the indep var Using an unreliable or insensitive dep measure Sample size is too small.

Choosing a Sample Size: Power Analysis
Sample size can be based on what is typical in that particular area of research Sample size can also be based on a desired probability of correctly rejecting the null hyp This probability is called the power of the statistical test  the sensitivity of the statistical procedure to detect differences in your data Power = 1 – p (Type II error ) Power analysis-computer generated Higher desired power demands a greater sample size Researchers usually use a power between .70 and .90 to determine sample size