2 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.
3 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.
4 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
5 Sample SizeThe 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 valueThus, as your sample size increases, you can be more confident that your outcome is actually different from the expectations of the null hyp
6 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 1It reflects all the possible outcomes we could expect if we compared the means of two groups and the null hypothesis is correct
7 T-test The calculated t value is a ratio of two aspects of the data The difference between the group meansThe variability within groupsGroup difference difference between your obtained meansUnder the null hypothesis you expect this difference to be 0.The value of t increases as the difference betweent your obtained sample means increasesWithin-group variability the amt of variability of scores about the mean
8 T-test Formula t = group difference within-group variability The numerator of the formula is the difference between the means of the two groupsThe denominator is the variance (s2) of each group divided by the number of Ss in the group, which are added togetherThe square root of the variance divided by the number of subjects = standard deviationFinally, we calculate our obtained t value by dividing the mean difference by the SDYou would then compare your obtained t to those listed in the t-table of critical values to determine if it is significant or not
9 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)
10 Two Tailed TestTwo-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 distributionThere are two critical region in a two-tailed testTo 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 casesSo the scores required to reach statistical significance must be more extreme than was necessary for the one-tailed test
11 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 testThe 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 directionThe general rule of thumb is: Always use a two-tailed test unless there are compelling
12 F-testThe analysis of variance or F test is an extension of the t testWhen a study has only one IV, F and t are virtually identical—the F = t-squaredANOVA is used when there are more than two levels of an independent variableThe 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 groupsError variance the deviation of the individual scores in each goup from their respective group meansThe larger the F value, the more likely the score is significant
13 Effect SizeEffect size quantifies the size of the difference between groupsIf 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
14 Type one and Type two errors Type I error occurs when the researcher says that a relationship exists when in fact it does notYou have falsely rejected the null hypType II error occurs when the researcher says that a relationship does not exist, when in fact it doesYou have falsely accepted the null hyp
15 True State of Affairs Null is true Null is False C Reject Null Type I erroralphaCorrect Decision1-betaAcceptNullCorrect Decision1-alphaType II errorbeta
16 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 errorTrue 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
17 Interpreting non-significant results Negative or nonsignificant results are difficult to interpretThere are several causes for nonsignificant results:The instruction could be hard to understandHave a weak manipulation of the indep varUsing an unreliable or insensitive dep measureSample size is too small.
18 Choosing a Sample Size: Power Analysis Sample size can be based on what is typical in that particular area of researchSample size can also be based on a desired probability of correctly rejecting the null hypThis probability is called the power of the statistical test the sensitivity of the statistical procedure to detect differences in your dataPower = 1 – p (Type II error )Power analysis-computer generatedHigher desired power demands a greater sample sizeResearchers usually use a power between .70 and .90 to determine sample size