Statistical Hypothesis Testing and Linear Regression
CHAPTER 4 – THE BASIS OF STATISTICAL TESTING * samples and populations * population – everyone in a specified target group rather than a specific region * sample – a selection of individuals from the population * sampling * simple random sampling – identify all the people in the target population and then randomly select the number that you need for your research * extremely difficult, time-consuming, expensive * cluster sampling – identify clustering units in the population * opportunity sampling – selecting participants who just happen to be available at the time and the place that you are conducting your research * snowball sampling – referrals from participants * volunteer sampling – where you might advertise your study and wait for people who have read your ad to come forward to take part * how generalizable are data? * Q: are the means for our sample approximately equal to the mean from the population? * randomly selected sample because of this random factor, sample may not be exactly representative * sampling error * the difference between the sample mean and the population mean * ensure that you have enough participants so that you get an accurate reflection of the population that you are interested in * population mean (parameter), sample mean (statistic) * the larger the samples, the closer to the population parameter the statistics will be * probabilities * the number possible outcomes that you are interested in divided by the total number of possible outcomes associated with an event * null hypothesis significance testing (NHST) * the null hypothesis states that there is no effect in the population of interest * if the probability of obtaining the data is high, the null hypothesis is true * no effect in the population *