To test the effect of a new exercise routine on muscle growth, a researcher sets up the following experiment: 60 volunteers subjects (40 men and 20 women) of average build are selected to participate in the study. Their body mass and muscle sizes (biceps, thighs, etc.) are measured. The researcher gives all the men a DVD with the new exercise routine, and the women a DVD with a standard routine, but only the researcher knows this.
The results of the experiment are likely to be invalid mostly because .
The treatment group and control group were not the same size.
The subjects were volunteers.
The subjects did not know if they were getting the real treatment.
The gender of the subject is a confounding variable in this situation.

Any quantity from a population sample is referred to as a sample statistic. A sample is a collection of components chosen at random from the population.
The parameters are the characteristics of the population, and statistics are the characteristics of the sample data. Both the population and the sample are crucial components of statistics.
By utilising sample statistics, inferential statistics provides strategies for generalising population features. Consider the following statistic: "50% of kindergarten students in India watch TV for more than 20 hours a week."
Not all kindergarten students in India were polled regarding their TV viewing habits in order to reach this conclusion. Instead, by using sample space to estimate the population parameters, this conclusion would have been reached.

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