Reply this Discussion question

A hypothesis is an informed guess and can be anything at all as long as it can be tested through experiment or observation (Hypothesis Testing, 2024). Using the word "if" and "then" supports a good hypothesis statement (Hypothesis Testing, 2024).

Example: It is hypothesized that if new home dialysis patients are provided five consecutive days of training then the number of peritonitis episodes will decrease.

Null Hypothesis: Providing five consecutive days of training for new home dialysis patients will not decrease the occurrences of peritonitis. (no relationship with training and peritonitis)

Alternative Hypothesis: Providing five consecutive days of training for new home dialysis patients will decrease the occurrences of peritonitis. (there is a relationship with training and peritonitis)

Once a study is completed and the data is analyzed a decision to reject the hypothesis is determined based on the probability that the results are not due to chance (Ambrose, 2022). This is never with one hundred percent certainty and the conclusion is always "probably" (Ambrose, 2022). This probability comes with risks for error to occur and are classified as type I or type II.

The type I error occurs when the researcher does not accept the null hypothesis when actually it was true (Ambrose, 2022). Type II error is the other side of this, when the researcher considers there was no effect on the population and in reality there was an effect (Ambrose, 2022). The null hypothesis is inaccurately accepted when it should have been rejected (Ambrose, 2022).

A Type I error would exist if the independent variable (training five consecutive days) was reported to have an effect on the episodes of peritonitis but there was actually no positive improvement. I rejected the null hypothesis and by doing so accepted the "false" idea that training for five consecutive days showed an improvement.

Type II error occurs when the researcher accepted the null hypothesis that training five consecutive days had not improved peritonitis rates when in fact it had.

The one-tailed test is designed to identify effects in a single direction (Ambrose, 2022). For my example, i would use a one-tailed test since my concern was whether the training decreased the peritonitis rates.