PSY516 Final Term Highlighted Handouts

Statistics in Psychology (PSY516)

Table of Content

Lesson no.

Title

Page no. Introduction to Statistics-I Introduction to Statistics-II Correlation Method Experimental and Non-experimental Method Introduction to SPSS Descriptive statistics Frequency Distribution-I Frequency Distribution-II Measure of Central Tendency-I Measure of Central Tendency-II Measure of Variability-I Measure of Variability-II Z-scores Introduction to Probability-I Introduction to Probability-II Introduction to Probability-III Sampling Distribution Confidence Interval-I Confidence Interval-II Hypothesis Testing-I Hypothesis Testing-II Hypothesis Testing-III

T-Test-I T-Test-II Independent Sample T-test-I Independent Sample T-test-II Repeated Measure T-Test Analysis of variance (ANOVA) One-way ANOVA Two-Way ANOVA-I Two-Way ANOVA-II Correlation Analysis-I Correlation Analysis-II Correlation Analysis-III Types of Correlation-I Introduction To Regression Simple Linear Regression Multiple Linear Regression Non-Parametric Test Chi-Square Test For Independence-I Chi-Square Test For Independence-II Mann-Whitney U-Test Wilcoxon Signed Rank Test Kruskal Wallis & Friedman Test

Lesson 23

Role Of Sample Size In Statistical Significance

Increasing sample size makes the hypothesis test more sensitive - more likely to reject the null hypothesis when it is, in fact, false. Thus, it increases the power of the test. The effect size is not affected by sample size. Higher sample size allows the researcher to increase the significance level of the findings, since the confidence of the result are likely to increase with a higher sample size. This is to be expected because larger the sample size, the more accurately it is expected to mirror the behavior of the whole group.

Statistical Significance

If 95% confidence intervals do not overlap then we can could conclude that the means come from different populations, and, therefore, that they are significantly different. Whether the results of a study are statistically significant can depend on the sample size. A larger sample size tends to yield a statistically significant results. On the other hand, if the sample size is too small, an important relationship or difference can go undetected. In that case, the power of the test is too low. Given that power is the ability of a test to find an effect that genuinely exists, and the effect is found by having a statistically significant result (i.e., p < 0.05). Hence, there is also a connection between the sample size and the p-value associated with a test statistic. We clear this concept by giving an example that is study got two groups of 10 heterosexual young men and got them to go up to a woman that they found attractive and either engage them in conversation (group 1) or sing them a song (group 2). Researchers measured how long it was before the woman ran away. Imagine the experiment was also repeated using 100 men in each group. Results showed that in both cases the singing group had a mean of 10 and a standard deviation of 3, and the conversation group had a mean of 12 and a standard deviation of 3. The only difference between the two experiments is that one collected 10 scores per sample, and the other 100 scores per sample. The confidence intervals become much narrower when the samples contain 100 scores than when they contain only 10 scores.